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Dr. Ji Son

Dr. Ji Son

Effect Size & Power

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Table of Contents

I. Introduction
Descriptive Statistics vs. Inferential Statistics

25m 31s

Intro
0:00
Roadmap
0:10
Roadmap
0:11
Statistics
0:35
Statistics
0:36
Let's Think About High School Science
1:12
Measurement and Find Patterns (Mathematical Formula)
1:13
Statistics = Math of Distributions
4:58
Distributions
4:59
Problematic… but also GREAT
5:58
Statistics
7:33
How is It Different from Other Specializations in Mathematics?
7:34
Statistics is Fundamental in Natural and Social Sciences
7:53
Two Skills of Statistics
8:20
Description (Exploration)
8:21
Inference
9:13
Descriptive Statistics vs. Inferential Statistics: Apply to Distributions
9:58
Descriptive Statistics
9:59
Inferential Statistics
11:05
Populations vs. Samples
12:19
Populations vs. Samples: Is it the Truth?
12:20
Populations vs. Samples: Pros & Cons
13:36
Populations vs. Samples: Descriptive Values
16:12
Putting Together Descriptive/Inferential Stats & Populations/Samples
17:10
Putting Together Descriptive/Inferential Stats & Populations/Samples
17:11
Example 1: Descriptive Statistics vs. Inferential Statistics
19:09
Example 2: Descriptive Statistics vs. Inferential Statistics
20:47
Example 3: Sample, Parameter, Population, and Statistic
21:40
Example 4: Sample, Parameter, Population, and Statistic
23:28
II. About Samples: Cases, Variables, Measurements
About Samples: Cases, Variables, Measurements

32m 14s

Intro
0:00
Data
0:09
Data, Cases, Variables, and Values
0:10
Rows, Columns, and Cells
2:03
Example: Aircrafts
3:52
How Do We Get Data?
5:38
Research: Question and Hypothesis
5:39
Research Design
7:11
Measurement
7:29
Research Analysis
8:33
Research Conclusion
9:30
Types of Variables
10:03
Discrete Variables
10:04
Continuous Variables
12:07
Types of Measurements
14:17
Types of Measurements
14:18
Types of Measurements (Scales)
17:22
Nominal
17:23
Ordinal
19:11
Interval
21:33
Ratio
24:24
Example 1: Cases, Variables, Measurements
25:20
Example 2: Which Scale of Measurement is Used?
26:55
Example 3: What Kind of a Scale of Measurement is This?
27:26
Example 4: Discrete vs. Continuous Variables.
30:31
III. Visualizing Distributions
Introduction to Excel

8m 9s

Intro
0:00
Before Visualizing Distribution
0:10
Excel
0:11
Excel: Organization
0:45
Workbook
0:46
Column x Rows
1:50
Tools: Menu Bar, Standard Toolbar, and Formula Bar
3:00
Excel + Data
6:07
Exce and Data
6:08
Frequency Distributions in Excel

39m 10s

Intro
0:00
Roadmap
0:08
Data in Excel and Frequency Distributions
0:09
Raw Data to Frequency Tables
0:42
Raw Data to Frequency Tables
0:43
Frequency Tables: Using Formulas and Pivot Tables
1:28
Example 1: Number of Births
7:17
Example 2: Age Distribution
20:41
Example 3: Height Distribution
27:45
Example 4: Height Distribution of Males
32:19
Frequency Distributions and Features

25m 29s

Intro
0:00
Roadmap
0:10
Data in Excel, Frequency Distributions, and Features of Frequency Distributions
0:11
Example #1
1:35
Uniform
1:36
Example #2
2:58
Unimodal, Skewed Right, and Asymmetric
2:59
Example #3
6:29
Bimodal
6:30
Example #4a
8:29
Symmetric, Unimodal, and Normal
8:30
Point of Inflection and Standard Deviation
11:13
Example #4b
12:43
Normal Distribution
12:44
Summary
13:56
Uniform, Skewed, Bimodal, and Normal
13:57
Sketch Problem 1: Driver's License
17:34
Sketch Problem 2: Life Expectancy
20:01
Sketch Problem 3: Telephone Numbers
22:01
Sketch Problem 4: Length of Time Used to Complete a Final Exam
23:43
Dotplots and Histograms in Excel

42m 42s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Previously
1:02
Data, Frequency Table, and visualization
1:03
Dotplots
1:22
Dotplots Excel Example
1:23
Dotplots: Pros and Cons
7:22
Pros and Cons of Dotplots
7:23
Dotplots Excel Example Cont.
9:07
Histograms
12:47
Histograms Overview
12:48
Example of Histograms
15:29
Histograms: Pros and Cons
31:39
Pros
31:40
Cons
32:31
Frequency vs. Relative Frequency
32:53
Frequency
32:54
Relative Frequency
33:36
Example 1: Dotplots vs. Histograms
34:36
Example 2: Age of Pennies Dotplot
36:21
Example 3: Histogram of Mammal Speeds
38:27
Example 4: Histogram of Life Expectancy
40:30
Stemplots

12m 23s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
What Sets Stemplots Apart?
0:46
Data Sets, Dotplots, Histograms, and Stemplots
0:47
Example 1: What Do Stemplots Look Like?
1:58
Example 2: Back-to-Back Stemplots
5:00
Example 3: Quiz Grade Stemplot
7:46
Example 4: Quiz Grade & Afterschool Tutoring Stemplot
9:56
Bar Graphs

22m 49s

Intro
0:00
Roadmap
0:05
Roadmap
0:08
Review of Frequency Distributions
0:44
Y-axis and X-axis
0:45
Types of Frequency Visualizations Covered so Far
2:16
Introduction to Bar Graphs
4:07
Example 1: Bar Graph
5:32
Example 1: Bar Graph
5:33
Do Shapes, Center, and Spread of Distributions Apply to Bar Graphs?
11:07
Do Shapes, Center, and Spread of Distributions Apply to Bar Graphs?
11:08
Example 2: Create a Frequency Visualization for Gender
14:02
Example 3: Cases, Variables, and Frequency Visualization
16:34
Example 4: What Kind of Graphs are Shown Below?
19:29
IV. Summarizing Distributions
Central Tendency: Mean, Median, Mode

38m 50s

Intro
0:00
Roadmap
0:07
Roadmap
0:08
Central Tendency 1
0:56
Way to Summarize a Distribution of Scores
0:57
Mode
1:32
Median
2:02
Mean
2:36
Central Tendency 2
3:47
Mode
3:48
Median
4:20
Mean
5:25
Summation Symbol
6:11
Summation Symbol
6:12
Population vs. Sample
10:46
Population vs. Sample
10:47
Excel Examples
15:08
Finding Mode, Median, and Mean in Excel
15:09
Median vs. Mean
21:45
Effect of Outliers
21:46
Relationship Between Parameter and Statistic
22:44
Type of Measurements
24:00
Which Distributions to Use With
24:55
Example 1: Mean
25:30
Example 2: Using Summation Symbol
29:50
Example 3: Average Calorie Count
32:50
Example 4: Creating an Example Set
35:46
Variability

42m 40s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Variability (or Spread)
0:45
Variability (or Spread)
0:46
Things to Think About
5:45
Things to Think About
5:46
Range, Quartiles and Interquartile Range
6:37
Range
6:38
Interquartile Range
8:42
Interquartile Range Example
10:58
Interquartile Range Example
10:59
Variance and Standard Deviation
12:27
Deviations
12:28
Sum of Squares
14:35
Variance
16:55
Standard Deviation
17:44
Sum of Squares (SS)
18:34
Sum of Squares (SS)
18:35
Population vs. Sample SD
22:00
Population vs. Sample SD
22:01
Population vs. Sample
23:20
Mean
23:21
SD
23:51
Example 1: Find the Mean and Standard Deviation of the Variable Friends in the Excel File
27:21
Example 2: Find the Mean and Standard Deviation of the Tagged Photos in the Excel File
35:25
Example 3: Sum of Squares
38:58
Example 4: Standard Deviation
41:48
Five Number Summary & Boxplots

57m 15s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Summarizing Distributions
0:37
Shape, Center, and Spread
0:38
5 Number Summary
1:14
Boxplot: Visualizing 5 Number Summary
3:37
Boxplot: Visualizing 5 Number Summary
3:38
Boxplots on Excel
9:01
Using 'Stocks' and Using Stacked Columns
9:02
Boxplots on Excel Example
10:14
When are Boxplots Useful?
32:14
Pros
32:15
Cons
32:59
How to Determine Outlier Status
33:24
Rule of Thumb: Upper Limit
33:25
Rule of Thumb: Lower Limit
34:16
Signal Outliers in an Excel Data File Using Conditional Formatting
34:52
Modified Boxplot
48:38
Modified Boxplot
48:39
Example 1: Percentage Values & Lower and Upper Whisker
49:10
Example 2: Boxplot
50:10
Example 3: Estimating IQR From Boxplot
53:46
Example 4: Boxplot and Missing Whisker
54:35
Shape: Calculating Skewness & Kurtosis

41m 51s

Intro
0:00
Roadmap
0:16
Roadmap
0:17
Skewness Concept
1:09
Skewness Concept
1:10
Calculating Skewness
3:26
Calculating Skewness
3:27
Interpreting Skewness
7:36
Interpreting Skewness
7:37
Excel Example
8:49
Kurtosis Concept
20:29
Kurtosis Concept
20:30
Calculating Kurtosis
24:17
Calculating Kurtosis
24:18
Interpreting Kurtosis
29:01
Leptokurtic
29:35
Mesokurtic
30:10
Platykurtic
31:06
Excel Example
32:04
Example 1: Shape of Distribution
38:28
Example 2: Shape of Distribution
39:29
Example 3: Shape of Distribution
40:14
Example 4: Kurtosis
41:10
Normal Distribution

34m 33s

Intro
0:00
Roadmap
0:13
Roadmap
0:14
What is a Normal Distribution
0:44
The Normal Distribution As a Theoretical Model
0:45
Possible Range of Probabilities
3:05
Possible Range of Probabilities
3:06
What is a Normal Distribution
5:07
Can Be Described By
5:08
Properties
5:49
'Same' Shape: Illusion of Different Shape!
7:35
'Same' Shape: Illusion of Different Shape!
7:36
Types of Problems
13:45
Example: Distribution of SAT Scores
13:46
Shape Analogy
19:48
Shape Analogy
19:49
Example 1: The Standard Normal Distribution and Z-Scores
22:34
Example 2: The Standard Normal Distribution and Z-Scores
25:54
Example 3: Sketching and Normal Distribution
28:55
Example 4: Sketching and Normal Distribution
32:32
Standard Normal Distributions & Z-Scores

41m 44s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
A Family of Distributions
0:28
Infinite Set of Distributions
0:29
Transforming Normal Distributions to 'Standard' Normal Distribution
1:04
Normal Distribution vs. Standard Normal Distribution
2:58
Normal Distribution vs. Standard Normal Distribution
2:59
Z-Score, Raw Score, Mean, & SD
4:08
Z-Score, Raw Score, Mean, & SD
4:09
Weird Z-Scores
9:40
Weird Z-Scores
9:41
Excel
16:45
For Normal Distributions
16:46
For Standard Normal Distributions
19:11
Excel Example
20:24
Types of Problems
25:18
Percentage Problem: P(x)
25:19
Raw Score and Z-Score Problems
26:28
Standard Deviation Problems
27:01
Shape Analogy
27:44
Shape Analogy
27:45
Example 1: Deaths Due to Heart Disease vs. Deaths Due to Cancer
28:24
Example 2: Heights of Male College Students
33:15
Example 3: Mean and Standard Deviation
37:14
Example 4: Finding Percentage of Values in a Standard Normal Distribution
37:49
Normal Distribution: PDF vs. CDF

55m 44s

Intro
0:00
Roadmap
0:15
Roadmap
0:16
Frequency vs. Cumulative Frequency
0:56
Frequency vs. Cumulative Frequency
0:57
Frequency vs. Cumulative Frequency
4:32
Frequency vs. Cumulative Frequency Cont.
4:33
Calculus in Brief
6:21
Derivative-Integral Continuum
6:22
PDF
10:08
PDF for Standard Normal Distribution
10:09
PDF for Normal Distribution
14:32
Integral of PDF = CDF
21:27
Integral of PDF = CDF
21:28
Example 1: Cumulative Frequency Graph
23:31
Example 2: Mean, Standard Deviation, and Probability
24:43
Example 3: Mean and Standard Deviation
35:50
Example 4: Age of Cars
49:32
V. Linear Regression
Scatterplots

47m 19s

Intro
0:00
Roadmap
0:04
Roadmap
0:05
Previous Visualizations
0:30
Frequency Distributions
0:31
Compare & Contrast
2:26
Frequency Distributions Vs. Scatterplots
2:27
Summary Values
4:53
Shape
4:54
Center & Trend
6:41
Spread & Strength
8:22
Univariate & Bivariate
10:25
Example Scatterplot
10:48
Shape, Trend, and Strength
10:49
Positive and Negative Association
14:05
Positive and Negative Association
14:06
Linearity, Strength, and Consistency
18:30
Linearity
18:31
Strength
19:14
Consistency
20:40
Summarizing a Scatterplot
22:58
Summarizing a Scatterplot
22:59
Example 1: Gapminder.org, Income x Life Expectancy
26:32
Example 2: Gapminder.org, Income x Infant Mortality
36:12
Example 3: Trend and Strength of Variables
40:14
Example 4: Trend, Strength and Shape for Scatterplots
43:27
Regression

32m 2s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Linear Equations
0:34
Linear Equations: y = mx + b
0:35
Rough Line
5:16
Rough Line
5:17
Regression - A 'Center' Line
7:41
Reasons for Summarizing with a Regression Line
7:42
Predictor and Response Variable
10:04
Goal of Regression
12:29
Goal of Regression
12:30
Prediction
14:50
Example: Servings of Mile Per Year Shown By Age
14:51
Intrapolation
17:06
Extrapolation
17:58
Error in Prediction
20:34
Prediction Error
20:35
Residual
21:40
Example 1: Residual
23:34
Example 2: Large and Negative Residual
26:30
Example 3: Positive Residual
28:13
Example 4: Interpret Regression Line & Extrapolate
29:40
Least Squares Regression

56m 36s

Intro
0:00
Roadmap
0:13
Roadmap
0:14
Best Fit
0:47
Best Fit
0:48
Sum of Squared Errors (SSE)
1:50
Sum of Squared Errors (SSE)
1:51
Why Squared?
3:38
Why Squared?
3:39
Quantitative Properties of Regression Line
4:51
Quantitative Properties of Regression Line
4:52
So How do we Find Such a Line?
6:49
SSEs of Different Line Equations & Lowest SSE
6:50
Carl Gauss' Method
8:01
How Do We Find Slope (b1)
11:00
How Do We Find Slope (b1)
11:01
Hoe Do We Find Intercept
15:11
Hoe Do We Find Intercept
15:12
Example 1: Which of These Equations Fit the Above Data Best?
17:18
Example 2: Find the Regression Line for These Data Points and Interpret It
26:31
Example 3: Summarize the Scatterplot and Find the Regression Line.
34:31
Example 4: Examine the Mean of Residuals
43:52
Correlation

43m 58s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Summarizing a Scatterplot Quantitatively
0:47
Shape
0:48
Trend
1:11
Strength: Correlation ®
1:45
Correlation Coefficient ( r )
2:30
Correlation Coefficient ( r )
2:31
Trees vs. Forest
11:59
Trees vs. Forest
12:00
Calculating r
15:07
Average Product of z-scores for x and y
15:08
Relationship between Correlation and Slope
21:10
Relationship between Correlation and Slope
21:11
Example 1: Find the Correlation between Grams of Fat and Cost
24:11
Example 2: Relationship between r and b1
30:24
Example 3: Find the Regression Line
33:35
Example 4: Find the Correlation Coefficient for this Set of Data
37:37
Correlation: r vs. r-squared

52m 52s

Intro
0:00
Roadmap
0:07
Roadmap
0:08
R-squared
0:44
What is the Meaning of It? Why Squared?
0:45
Parsing Sum of Squared (Parsing Variability)
2:25
SST = SSR + SSE
2:26
What is SST and SSE?
7:46
What is SST and SSE?
7:47
r-squared
18:33
Coefficient of Determination
18:34
If the Correlation is Strong…
20:25
If the Correlation is Strong…
20:26
If the Correlation is Weak…
22:36
If the Correlation is Weak…
22:37
Example 1: Find r-squared for this Set of Data
23:56
Example 2: What Does it Mean that the Simple Linear Regression is a 'Model' of Variance?
33:54
Example 3: Why Does r-squared Only Range from 0 to 1
37:29
Example 4: Find the r-squared for This Set of Data
39:55
Transformations of Data

27m 8s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Why Transform?
0:26
Why Transform?
0:27
Shape-preserving vs. Shape-changing Transformations
5:14
Shape-preserving = Linear Transformations
5:15
Shape-changing Transformations = Non-linear Transformations
6:20
Common Shape-Preserving Transformations
7:08
Common Shape-Preserving Transformations
7:09
Common Shape-Changing Transformations
8:59
Powers
9:00
Logarithms
9:39
Change Just One Variable? Both?
10:38
Log-log Transformations
10:39
Log Transformations
14:38
Example 1: Create, Graph, and Transform the Data Set
15:19
Example 2: Create, Graph, and Transform the Data Set
20:08
Example 3: What Kind of Model would You Choose for this Data?
22:44
Example 4: Transformation of Data
25:46
VI. Collecting Data in an Experiment
Sampling & Bias

54m 44s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Descriptive vs. Inferential Statistics
1:04
Descriptive Statistics: Data Exploration
1:05
Example
2:03
To tackle Generalization…
4:31
Generalization
4:32
Sampling
6:06
'Good' Sample
6:40
Defining Samples and Populations
8:55
Population
8:56
Sample
11:16
Why Use Sampling?
13:09
Why Use Sampling?
13:10
Goal of Sampling: Avoiding Bias
15:04
What is Bias?
15:05
Where does Bias Come from: Sampling Bias
17:53
Where does Bias Come from: Response Bias
18:27
Sampling Bias: Bias from Bas Sampling Methods
19:34
Size Bias
19:35
Voluntary Response Bias
21:13
Convenience Sample
22:22
Judgment Sample
23:58
Inadequate Sample Frame
25:40
Response Bias: Bias from 'Bad' Data Collection Methods
28:00
Nonresponse Bias
29:31
Questionnaire Bias
31:10
Incorrect Response or Measurement Bias
37:32
Example 1: What Kind of Biases?
40:29
Example 2: What Biases Might Arise?
44:46
Example 3: What Kind of Biases?
48:34
Example 4: What Kind of Biases?
51:43
Sampling Methods

14m 25s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Biased vs. Unbiased Sampling Methods
0:32
Biased Sampling
0:33
Unbiased Sampling
1:13
Probability Sampling Methods
2:31
Simple Random
2:54
Stratified Random Sampling
4:06
Cluster Sampling
5:24
Two-staged Sampling
6:22
Systematic Sampling
7:25
Example 1: Which Type(s) of Sampling was this?
8:33
Example 2: Describe How to Take a Two-Stage Sample from this Book
10:16
Example 3: Sampling Methods
11:58
Example 4: Cluster Sample Plan
12:48
Research Design

53m 54s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Descriptive vs. Inferential Statistics
0:51
Descriptive Statistics: Data Exploration
0:52
Inferential Statistics
1:02
Variables and Relationships
1:44
Variables
1:45
Relationships
2:49
Not Every Type of Study is an Experiment…
4:16
Category I - Descriptive Study
4:54
Category II - Correlational Study
5:50
Category III - Experimental, Quasi-experimental, Non-experimental
6:33
Category III
7:42
Experimental, Quasi-experimental, and Non-experimental
7:43
Why CAN'T the Other Strategies Determine Causation?
10:18
Third-variable Problem
10:19
Directionality Problem
15:49
What Makes Experiments Special?
17:54
Manipulation
17:55
Control (and Comparison)
21:58
Methods of Control
26:38
Holding Constant
26:39
Matching
29:11
Random Assignment
31:48
Experiment Terminology
34:09
'true' Experiment vs. Study
34:10
Independent Variable (IV)
35:16
Dependent Variable (DV)
35:45
Factors
36:07
Treatment Conditions
36:23
Levels
37:43
Confounds or Extraneous Variables
38:04
Blind
38:38
Blind Experiments
38:39
Double-blind Experiments
39:29
How Categories Relate to Statistics
41:35
Category I - Descriptive Study
41:36
Category II - Correlational Study
42:05
Category III - Experimental, Quasi-experimental, Non-experimental
42:43
Example 1: Research Design
43:50
Example 2: Research Design
47:37
Example 3: Research Design
50:12
Example 4: Research Design
52:00
Between and Within Treatment Variability

41m 31s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Experimental Designs
0:51
Experimental Designs: Manipulation & Control
0:52
Two Types of Variability
2:09
Between Treatment Variability
2:10
Within Treatment Variability
3:31
Updated Goal of Experimental Design
5:47
Updated Goal of Experimental Design
5:48
Example: Drugs and Driving
6:56
Example: Drugs and Driving
6:57
Different Types of Random Assignment
11:27
All Experiments
11:28
Completely Random Design
12:02
Randomized Block Design
13:19
Randomized Block Design
15:48
Matched Pairs Design
15:49
Repeated Measures Design
19:47
Between-subject Variable vs. Within-subject Variable
22:43
Completely Randomized Design
22:44
Repeated Measures Design
25:03
Example 1: Design a Completely Random, Matched Pair, and Repeated Measures Experiment
26:16
Example 2: Block Design
31:41
Example 3: Completely Randomized Designs
35:11
Example 4: Completely Random, Matched Pairs, or Repeated Measures Experiments?
39:01
VII. Review of Probability Axioms
Sample Spaces

37m 52s

Intro
0:00
Roadmap
0:07
Roadmap
0:08
Why is Probability Involved in Statistics
0:48
Probability
0:49
Can People Tell the Difference between Cheap and Gourmet Coffee?
2:08
Taste Test with Coffee Drinkers
3:37
If No One can Actually Taste the Difference
3:38
If Everyone can Actually Taste the Difference
5:36
Creating a Probability Model
7:09
Creating a Probability Model
7:10
D'Alembert vs. Necker
9:41
D'Alembert vs. Necker
9:42
Problem with D'Alembert's Model
13:29
Problem with D'Alembert's Model
13:30
Covering Entire Sample Space
15:08
Fundamental Principle of Counting
15:09
Where Do Probabilities Come From?
22:54
Observed Data, Symmetry, and Subjective Estimates
22:55
Checking whether Model Matches Real World
24:27
Law of Large Numbers
24:28
Example 1: Law of Large Numbers
27:46
Example 2: Possible Outcomes
30:43
Example 3: Brands of Coffee and Taste
33:25
Example 4: How Many Different Treatments are there?
35:33
Addition Rule for Disjoint Events

20m 29s

Intro
0:00
Roadmap
0:08
Roadmap
0:09
Disjoint Events
0:41
Disjoint Events
0:42
Meaning of 'or'
2:39
In Regular Life
2:40
In Math/Statistics/Computer Science
3:10
Addition Rule for Disjoin Events
3:55
If A and B are Disjoint: P (A and B)
3:56
If A and B are Disjoint: P (A or B)
5:15
General Addition Rule
5:41
General Addition Rule
5:42
Generalized Addition Rule
8:31
If A and B are not Disjoint: P (A or B)
8:32
Example 1: Which of These are Mutually Exclusive?
10:50
Example 2: What is the Probability that You will Have a Combination of One Heads and Two Tails?
12:57
Example 3: Engagement Party
15:17
Example 4: Home Owner's Insurance
18:30
Conditional Probability

57m 19s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
'or' vs. 'and' vs. Conditional Probability
1:07
'or' vs. 'and' vs. Conditional Probability
1:08
'and' vs. Conditional Probability
5:57
P (M or L)
5:58
P (M and L)
8:41
P (M|L)
11:04
P (L|M)
12:24
Tree Diagram
15:02
Tree Diagram
15:03
Defining Conditional Probability
22:42
Defining Conditional Probability
22:43
Common Contexts for Conditional Probability
30:56
Medical Testing: Positive Predictive Value
30:57
Medical Testing: Sensitivity
33:03
Statistical Tests
34:27
Example 1: Drug and Disease
36:41
Example 2: Marbles and Conditional Probability
40:04
Example 3: Cards and Conditional Probability
45:59
Example 4: Votes and Conditional Probability
50:21
Independent Events

24m 27s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Independent Events & Conditional Probability
0:26
Non-independent Events
0:27
Independent Events
2:00
Non-independent and Independent Events
3:08
Non-independent and Independent Events
3:09
Defining Independent Events
5:52
Defining Independent Events
5:53
Multiplication Rule
7:29
Previously…
7:30
But with Independent Evens
8:53
Example 1: Which of These Pairs of Events are Independent?
11:12
Example 2: Health Insurance and Probability
15:12
Example 3: Independent Events
17:42
Example 4: Independent Events
20:03
VIII. Probability Distributions
Introduction to Probability Distributions

56m 45s

Intro
0:00
Roadmap
0:08
Roadmap
0:09
Sampling vs. Probability
0:57
Sampling
0:58
Missing
1:30
What is Missing?
3:06
Insight: Probability Distributions
5:26
Insight: Probability Distributions
5:27
What is a Probability Distribution?
7:29
From Sample Spaces to Probability Distributions
8:44
Sample Space
8:45
Probability Distribution of the Sum of Two Die
11:16
The Random Variable
17:43
The Random Variable
17:44
Expected Value
21:52
Expected Value
21:53
Example 1: Probability Distributions
28:45
Example 2: Probability Distributions
35:30
Example 3: Probability Distributions
43:37
Example 4: Probability Distributions
47:20
Expected Value & Variance of Probability Distributions

53m 41s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Discrete vs. Continuous Random Variables
1:04
Discrete vs. Continuous Random Variables
1:05
Mean and Variance Review
4:44
Mean: Sample, Population, and Probability Distribution
4:45
Variance: Sample, Population, and Probability Distribution
9:12
Example Situation
14:10
Example Situation
14:11
Some Special Cases…
16:13
Some Special Cases…
16:14
Linear Transformations
19:22
Linear Transformations
19:23
What Happens to Mean and Variance of the Probability Distribution?
20:12
n Independent Values of X
25:38
n Independent Values of X
25:39
Compare These Two Situations
30:56
Compare These Two Situations
30:57
Two Random Variables, X and Y
32:02
Two Random Variables, X and Y
32:03
Example 1: Expected Value & Variance of Probability Distributions
35:35
Example 2: Expected Values & Standard Deviation
44:17
Example 3: Expected Winnings and Standard Deviation
48:18
Binomial Distribution

55m 15s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Discrete Probability Distributions
1:42
Discrete Probability Distributions
1:43
Binomial Distribution
2:36
Binomial Distribution
2:37
Multiplicative Rule Review
6:54
Multiplicative Rule Review
6:55
How Many Outcomes with k 'Successes'
10:23
Adults and Bachelor's Degree: Manual List of Outcomes
10:24
P (X=k)
19:37
Putting Together # of Outcomes with the Multiplicative Rule
19:38
Expected Value and Standard Deviation in a Binomial Distribution
25:22
Expected Value and Standard Deviation in a Binomial Distribution
25:23
Example 1: Coin Toss
33:42
Example 2: College Graduates
38:03
Example 3: Types of Blood and Probability
45:39
Example 4: Expected Number and Standard Deviation
51:11
IX. Sampling Distributions of Statistics
Introduction to Sampling Distributions

48m 17s

Intro
0:00
Roadmap
0:08
Roadmap
0:09
Probability Distributions vs. Sampling Distributions
0:55
Probability Distributions vs. Sampling Distributions
0:56
Same Logic
3:55
Logic of Probability Distribution
3:56
Example: Rolling Two Die
6:56
Simulating Samples
9:53
To Come Up with Probability Distributions
9:54
In Sampling Distributions
11:12
Connecting Sampling and Research Methods with Sampling Distributions
12:11
Connecting Sampling and Research Methods with Sampling Distributions
12:12
Simulating a Sampling Distribution
14:14
Experimental Design: Regular Sleep vs. Less Sleep
14:15
Logic of Sampling Distributions
23:08
Logic of Sampling Distributions
23:09
General Method of Simulating Sampling Distributions
25:38
General Method of Simulating Sampling Distributions
25:39
Questions that Remain
28:45
Questions that Remain
28:46
Example 1: Mean and Standard Error of Sampling Distribution
30:57
Example 2: What is the Best Way to Describe Sampling Distributions?
37:12
Example 3: Matching Sampling Distributions
38:21
Example 4: Mean and Standard Error of Sampling Distribution
41:51
Sampling Distribution of the Mean

1h 8m 48s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Special Case of General Method for Simulating a Sampling Distribution
1:53
Special Case of General Method for Simulating a Sampling Distribution
1:54
Computer Simulation
3:43
Using Simulations to See Principles behind Shape of SDoM
15:50
Using Simulations to See Principles behind Shape of SDoM
15:51
Conditions
17:38
Using Simulations to See Principles behind Center (Mean) of SDoM
20:15
Using Simulations to See Principles behind Center (Mean) of SDoM
20:16
Conditions: Does n Matter?
21:31
Conditions: Does Number of Simulation Matter?
24:37
Using Simulations to See Principles behind Standard Deviation of SDoM
27:13
Using Simulations to See Principles behind Standard Deviation of SDoM
27:14
Conditions: Does n Matter?
34:45
Conditions: Does Number of Simulation Matter?
36:24
Central Limit Theorem
37:13
SHAPE
38:08
CENTER
39:34
SPREAD
39:52
Comparing Population, Sample, and SDoM
43:10
Comparing Population, Sample, and SDoM
43:11
Answering the 'Questions that Remain'
48:24
What Happens When We Don't Know What the Population Looks Like?
48:25
Can We Have Sampling Distributions for Summary Statistics Other than the Mean?
49:42
How Do We Know whether a Sample is Sufficiently Unlikely?
53:36
Do We Always Have to Simulate a Large Number of Samples in Order to get a Sampling Distribution?
54:40
Example 1: Mean Batting Average
55:25
Example 2: Mean Sampling Distribution and Standard Error
59:07
Example 3: Sampling Distribution of the Mean
1:01:04
Sampling Distribution of Sample Proportions

54m 37s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Intro to Sampling Distribution of Sample Proportions (SDoSP)
0:51
Categorical Data (Examples)
0:52
Wish to Estimate Proportion of Population from Sample…
2:00
Notation
3:34
Population Proportion and Sample Proportion Notations
3:35
What's the Difference?
9:19
SDoM vs. SDoSP: Type of Data
9:20
SDoM vs. SDoSP: Shape
11:24
SDoM vs. SDoSP: Center
12:30
SDoM vs. SDoSP: Spread
15:34
Binomial Distribution vs. Sampling Distribution of Sample Proportions
19:14
Binomial Distribution vs. SDoSP: Type of Data
19:17
Binomial Distribution vs. SDoSP: Shape
21:07
Binomial Distribution vs. SDoSP: Center
21:43
Binomial Distribution vs. SDoSP: Spread
24:08
Example 1: Sampling Distribution of Sample Proportions
26:07
Example 2: Sampling Distribution of Sample Proportions
37:58
Example 3: Sampling Distribution of Sample Proportions
44:42
Example 4: Sampling Distribution of Sample Proportions
45:57
X. Inferential Statistics
Introduction to Confidence Intervals

42m 53s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Inferential Statistics
0:50
Inferential Statistics
0:51
Two Problems with This Picture…
3:20
Two Problems with This Picture…
3:21
Solution: Confidence Intervals (CI)
4:59
Solution: Hypotheiss Testing (HT)
5:49
Which Parameters are Known?
6:45
Which Parameters are Known?
6:46
Confidence Interval - Goal
7:56
When We Don't Know m but know s
7:57
When We Don't Know
18:27
When We Don't Know m nor s
18:28
Example 1: Confidence Intervals
26:18
Example 2: Confidence Intervals
29:46
Example 3: Confidence Intervals
32:18
Example 4: Confidence Intervals
38:31
t Distributions

1h 2m 6s

Intro
0:00
Roadmap
0:04
Roadmap
0:05
When to Use z vs. t?
1:07
When to Use z vs. t?
1:08
What is z and t?
3:02
z-score and t-score: Commonality
3:03
z-score and t-score: Formulas
3:34
z-score and t-score: Difference
5:22
Why not z? (Why t?)
7:24
Why not z? (Why t?)
7:25
But Don't Worry!
15:13
Gossett and t-distributions
15:14
Rules of t Distributions
17:05
t-distributions are More Normal as n Gets Bigger
17:06
t-distributions are a Family of Distributions
18:55
Degrees of Freedom (df)
20:02
Degrees of Freedom (df)
20:03
t Family of Distributions
24:07
t Family of Distributions : df = 2 , 4, and 60
24:08
df = 60
29:16
df = 2
29:59
How to Find It?
31:01
'Student's t-distribution' or 't-distribution'
31:02
Excel Example
33:06
Example 1: Which Distribution Do You Use? Z or t?
45:26
Example 2: Friends on Facebook
47:41
Example 3: t Distributions
52:15
Example 4: t Distributions , confidence interval, and mean
55:59
Introduction to Hypothesis Testing

1h 6m 33s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Issues to Overcome in Inferential Statistics
1:35
Issues to Overcome in Inferential Statistics
1:36
What Happens When We Don't Know What the Population Looks Like?
2:57
How Do We Know whether a sample is Sufficiently Unlikely
3:43
Hypothesizing a Population
6:44
Hypothesizing a Population
6:45
Null Hypothesis
8:07
Alternative Hypothesis
8:56
Hypotheses
11:58
Hypotheses
11:59
Errors in Hypothesis Testing
14:22
Errors in Hypothesis Testing
14:23
Steps of Hypothesis Testing
21:15
Steps of Hypothesis Testing
21:16
Single Sample HT ( When Sigma Available)
26:08
Example: Average Facebook Friends
26:09
Step1
27:08
Step 2
27:58
Step 3
28:17
Step 4
32:18
Single Sample HT (When Sigma Not Available)
36:33
Example: Average Facebook Friends
36:34
Step1: Hypothesis Testing
36:58
Step 2: Significance Level
37:25
Step 3: Decision Stage
37:40
Step 4: Sample
41:36
Sigma and p-value
45:04
Sigma and p-value
45:05
On tailed vs. Two Tailed Hypotheses
45:51
Example 1: Hypothesis Testing
48:37
Example 2: Heights of Women in the US
57:43
Example 3: Select the Best Way to Complete This Sentence
1:03:23
Confidence Intervals for the Difference of Two Independent Means

55m 14s

Intro
0:00
Roadmap
0:14
Roadmap
0:15
One Mean vs. Two Means
1:17
One Mean vs. Two Means
1:18
Notation
2:41
A Sample! A Set!
2:42
Mean of X, Mean of Y, and Difference of Two Means
3:56
SE of X
4:34
SE of Y
6:28
Sampling Distribution of the Difference between Two Means (SDoD)
7:48
Sampling Distribution of the Difference between Two Means (SDoD)
7:49
Rules of the SDoD (similar to CLT!)
15:00
Mean for the SDoD Null Hypothesis
15:01
Standard Error
17:39
When can We Construct a CI for the Difference between Two Means?
21:28
Three Conditions
21:29
Finding CI
23:56
One Mean CI
23:57
Two Means CI
25:45
Finding t
29:16
Finding t
29:17
Interpreting CI
30:25
Interpreting CI
30:26
Better Estimate of s (s pool)
34:15
Better Estimate of s (s pool)
34:16
Example 1: Confidence Intervals
42:32
Example 2: SE of the Difference
52:36
Hypothesis Testing for the Difference of Two Independent Means

50m

Intro
0:00
Roadmap
0:06
Roadmap
0:07
The Goal of Hypothesis Testing
0:56
One Sample and Two Samples
0:57
Sampling Distribution of the Difference between Two Means (SDoD)
3:42
Sampling Distribution of the Difference between Two Means (SDoD)
3:43
Rules of the SDoD (Similar to CLT!)
6:46
Shape
6:47
Mean for the Null Hypothesis
7:26
Standard Error for Independent Samples (When Variance is Homogenous)
8:18
Standard Error for Independent Samples (When Variance is not Homogenous)
9:25
Same Conditions for HT as for CI
10:08
Three Conditions
10:09
Steps of Hypothesis Testing
11:04
Steps of Hypothesis Testing
11:05
Formulas that Go with Steps of Hypothesis Testing
13:21
Step 1
13:25
Step 2
14:18
Step 3
15:00
Step 4
16:57
Example 1: Hypothesis Testing for the Difference of Two Independent Means
18:47
Example 2: Hypothesis Testing for the Difference of Two Independent Means
33:55
Example 3: Hypothesis Testing for the Difference of Two Independent Means
44:22
Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means

1h 14m 11s

Intro
0:00
Roadmap
0:09
Roadmap
0:10
The Goal of Hypothesis Testing
1:27
One Sample and Two Samples
1:28
Independent Samples vs. Paired Samples
3:16
Independent Samples vs. Paired Samples
3:17
Which is Which?
5:20
Independent SAMPLES vs. Independent VARIABLES
7:43
independent SAMPLES vs. Independent VARIABLES
7:44
T-tests Always…
10:48
T-tests Always…
10:49
Notation for Paired Samples
12:59
Notation for Paired Samples
13:00
Steps of Hypothesis Testing for Paired Samples
16:13
Steps of Hypothesis Testing for Paired Samples
16:14
Rules of the SDoD (Adding on Paired Samples)
18:03
Shape
18:04
Mean for the Null Hypothesis
18:31
Standard Error for Independent Samples (When Variance is Homogenous)
19:25
Standard Error for Paired Samples
20:39
Formulas that go with Steps of Hypothesis Testing
22:59
Formulas that go with Steps of Hypothesis Testing
23:00
Confidence Intervals for Paired Samples
30:32
Confidence Intervals for Paired Samples
30:33
Example 1: Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means
32:28
Example 2: Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means
44:02
Example 3: Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means
52:23
Type I and Type II Errors

31m 27s

Intro
0:00
Roadmap
0:18
Roadmap
0:19
Errors and Relationship to HT and the Sample Statistic?
1:11
Errors and Relationship to HT and the Sample Statistic?
1:12
Instead of a Box…Distributions!
7:00
One Sample t-test: Friends on Facebook
7:01
Two Sample t-test: Friends on Facebook
13:46
Usually, Lots of Overlap between Null and Alternative Distributions
16:59
Overlap between Null and Alternative Distributions
17:00
How Distributions and 'Box' Fit Together
22:45
How Distributions and 'Box' Fit Together
22:46
Example 1: Types of Errors
25:54
Example 2: Types of Errors
27:30
Example 3: What is the Danger of the Type I Error?
29:38
Effect Size & Power

44m 41s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Distance between Distributions: Sample t
0:49
Distance between Distributions: Sample t
0:50
Problem with Distance in Terms of Standard Error
2:56
Problem with Distance in Terms of Standard Error
2:57
Test Statistic (t) vs. Effect Size (d or g)
4:38
Test Statistic (t) vs. Effect Size (d or g)
4:39
Rules of Effect Size
6:09
Rules of Effect Size
6:10
Why Do We Need Effect Size?
8:21
Tells You the Practical Significance
8:22
HT can be Deceiving…
10:25
Important Note
10:42
What is Power?
11:20
What is Power?
11:21
Why Do We Need Power?
14:19
Conditional Probability and Power
14:20
Power is:
16:27
Can We Calculate Power?
19:00
Can We Calculate Power?
19:01
How Does Alpha Affect Power?
20:36
How Does Alpha Affect Power?
20:37
How Does Effect Size Affect Power?
25:38
How Does Effect Size Affect Power?
25:39
How Does Variability and Sample Size Affect Power?
27:56
How Does Variability and Sample Size Affect Power?
27:57
How Do We Increase Power?
32:47
Increasing Power
32:48
Example 1: Effect Size & Power
35:40
Example 2: Effect Size & Power
37:38
Example 3: Effect Size & Power
40:55
XI. Analysis of Variance
F-distributions

24m 46s

Intro
0:00
Roadmap
0:04
Roadmap
0:05
Z- & T-statistic and Their Distribution
0:34
Z- & T-statistic and Their Distribution
0:35
F-statistic
4:55
The F Ration ( the Variance Ratio)
4:56
F-distribution
12:29
F-distribution
12:30
s and p-value
15:00
s and p-value
15:01
Example 1: Why Does F-distribution Stop At 0 But Go On Until Infinity?
18:33
Example 2: F-distributions
19:29
Example 3: F-distributions and Heights
21:29
ANOVA with Independent Samples

1h 9m 25s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
The Limitations of t-tests
1:12
The Limitations of t-tests
1:13
Two Major Limitations of Many t-tests
3:26
Two Major Limitations of Many t-tests
3:27
Ronald Fisher's Solution… F-test! New Null Hypothesis
4:43
Ronald Fisher's Solution… F-test! New Null Hypothesis (Omnibus Test - One Test to Rule Them All!)
4:44
Analysis of Variance (ANoVA) Notation
7:47
Analysis of Variance (ANoVA) Notation
7:48
Partitioning (Analyzing) Variance
9:58
Total Variance
9:59
Within-group Variation
14:00
Between-group Variation
16:22
Time out: Review Variance & SS
17:05
Time out: Review Variance & SS
17:06
F-statistic
19:22
The F Ratio (the Variance Ratio)
19:23
S²bet = SSbet / dfbet
22:13
What is This?
22:14
How Many Means?
23:20
So What is the dfbet?
23:38
So What is SSbet?
24:15
S²w = SSw / dfw
26:05
What is This?
26:06
How Many Means?
27:20
So What is the dfw?
27:36
So What is SSw?
28:18
Chart of Independent Samples ANOVA
29:25
Chart of Independent Samples ANOVA
29:26
Example 1: Who Uploads More Photos: Unknown Ethnicity, Latino, Asian, Black, or White Facebook Users?
35:52
Hypotheses
35:53
Significance Level
39:40
Decision Stage
40:05
Calculate Samples' Statistic and p-Value
44:10
Reject or Fail to Reject H0
55:54
Example 2: ANOVA with Independent Samples
58:21
Repeated Measures ANOVA

1h 15m 13s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
The Limitations of t-tests
0:36
Who Uploads more Pictures and Which Photo-Type is Most Frequently Used on Facebook?
0:37
ANOVA (F-test) to the Rescue!
5:49
Omnibus Hypothesis
5:50
Analyze Variance
7:27
Independent Samples vs. Repeated Measures
9:12
Same Start
9:13
Independent Samples ANOVA
10:43
Repeated Measures ANOVA
12:00
Independent Samples ANOVA
16:00
Same Start: All the Variance Around Grand Mean
16:01
Independent Samples
16:23
Repeated Measures ANOVA
18:18
Same Start: All the Variance Around Grand Mean
18:19
Repeated Measures
18:33
Repeated Measures F-statistic
21:22
The F Ratio (The Variance Ratio)
21:23
S²bet = SSbet / dfbet
23:07
What is This?
23:08
How Many Means?
23:39
So What is the dfbet?
23:54
So What is SSbet?
24:32
S² resid = SS resid / df resid
25:46
What is This?
25:47
So What is SS resid?
26:44
So What is the df resid?
27:36
SS subj and df subj
28:11
What is This?
28:12
How Many Subject Means?
29:43
So What is df subj?
30:01
So What is SS subj?
30:09
SS total and df total
31:42
What is This?
31:43
What is the Total Number of Data Points?
32:02
So What is df total?
32:34
so What is SS total?
32:47
Chart of Repeated Measures ANOVA
33:19
Chart of Repeated Measures ANOVA: F and Between-samples Variability
33:20
Chart of Repeated Measures ANOVA: Total Variability, Within-subject (case) Variability, Residual Variability
35:50
Example 1: Which is More Prevalent on Facebook: Tagged, Uploaded, Mobile, or Profile Photos?
40:25
Hypotheses
40:26
Significance Level
41:46
Decision Stage
42:09
Calculate Samples' Statistic and p-Value
46:18
Reject or Fail to Reject H0
57:55
Example 2: Repeated Measures ANOVA
58:57
Example 3: What's the Problem with a Bunch of Tiny t-tests?
1:13:59
XII. Chi-square Test
Chi-Square Goodness-of-Fit Test

58m 23s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Where Does the Chi-Square Test Belong?
0:50
Where Does the Chi-Square Test Belong?
0:51
A New Twist on HT: Goodness-of-Fit
7:23
HT in General
7:24
Goodness-of-Fit HT
8:26
Hypotheses about Proportions
12:17
Null Hypothesis
12:18
Alternative Hypothesis
13:23
Example
14:38
Chi-Square Statistic
17:52
Chi-Square Statistic
17:53
Chi-Square Distributions
24:31
Chi-Square Distributions
24:32
Conditions for Chi-Square
28:58
Condition 1
28:59
Condition 2
30:20
Condition 3
30:32
Condition 4
31:47
Example 1: Chi-Square Goodness-of-Fit Test
32:23
Example 2: Chi-Square Goodness-of-Fit Test
44:34
Example 3: Which of These Statements Describe Properties of the Chi-Square Goodness-of-Fit Test?
56:06
Chi-Square Test of Homogeneity

51m 36s

Intro
0:00
Roadmap
0:09
Roadmap
0:10
Goodness-of-Fit vs. Homogeneity
1:13
Goodness-of-Fit HT
1:14
Homogeneity
2:00
Analogy
2:38
Hypotheses About Proportions
5:00
Null Hypothesis
5:01
Alternative Hypothesis
6:11
Example
6:33
Chi-Square Statistic
10:12
Same as Goodness-of-Fit Test
10:13
Set Up Data
12:28
Setting Up Data Example
12:29
Expected Frequency
16:53
Expected Frequency
16:54
Chi-Square Distributions & df
19:26
Chi-Square Distributions & df
19:27
Conditions for Test of Homogeneity
20:54
Condition 1
20:55
Condition 2
21:39
Condition 3
22:05
Condition 4
22:23
Example 1: Chi-Square Test of Homogeneity
22:52
Example 2: Chi-Square Test of Homogeneity
32:10
XIII. Overview of Statistics
Overview of Statistics

18m 11s

Intro
0:00
Roadmap
0:07
Roadmap
0:08
The Statistical Tests (HT) We've Covered
0:28
The Statistical Tests (HT) We've Covered
0:29
Organizing the Tests We've Covered…
1:08
One Sample: Continuous DV and Categorical DV
1:09
Two Samples: Continuous DV and Categorical DV
5:41
More Than Two Samples: Continuous DV and Categorical DV
8:21
The Following Data: OK Cupid
10:10
The Following Data: OK Cupid
10:11
Example 1: Weird-MySpace-Angle Profile Photo
10:38
Example 2: Geniuses
12:30
Example 3: Promiscuous iPhone Users
13:37
Example 4: Women, Aging, and Messaging
16:07
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Lecture Comments (2)

1 answer

Last reply by: Professor Son
Wed Nov 12, 2014 4:31 PM

Post by Brijesh Bolar on August 24, 2012

So how different is g and d from z.. I am getting a bit confused here..

Effect Size & Power

Lecture Slides are screen-captured images of important points in the lecture. Students can download and print out these lecture slide images to do practice problems as well as take notes while watching the lecture.

  • Intro 0:00
  • Roadmap 0:05
    • Roadmap
  • Distance between Distributions: Sample t 0:49
    • Distance between Distributions: Sample t
  • Problem with Distance in Terms of Standard Error 2:56
    • Problem with Distance in Terms of Standard Error
  • Test Statistic (t) vs. Effect Size (d or g) 4:38
    • Test Statistic (t) vs. Effect Size (d or g)
  • Rules of Effect Size 6:09
    • Rules of Effect Size
  • Why Do We Need Effect Size? 8:21
    • Tells You the Practical Significance
    • HT can be Deceiving…
    • Important Note
  • What is Power? 11:20
    • What is Power?
  • Why Do We Need Power? 14:19
    • Conditional Probability and Power
    • Power is:
  • Can We Calculate Power? 19:00
    • Can We Calculate Power?
  • How Does Alpha Affect Power? 20:36
    • How Does Alpha Affect Power?
  • How Does Effect Size Affect Power? 25:38
    • How Does Effect Size Affect Power?
  • How Does Variability and Sample Size Affect Power? 27:56
    • How Does Variability and Sample Size Affect Power?
  • How Do We Increase Power? 32:47
    • Increasing Power
  • Example 1: Effect Size & Power 35:40
  • Example 2: Effect Size & Power 37:38
  • Example 3: Effect Size & Power 40:55

Transcription: Effect Size & Power

Hi, welcome to educator.com.0000

We are going to talk about effect size and power.0002

So effect size and power, 2 things you need to think about whenever you do hypothesis testing.0005

So first effect size.0011

We are going to talk about what effect sizes is by contrasting it to the T statistic.0013

They actually have a lot in common but there is just one subtle difference that makes a huge difference.0019

Then we are going to talk about the rules of effect size and why we need effect size.0026

Then we are going to talk about power.0032

What is it, why do we need it, and how do all these different things affect power for instance sample size,0035

effect size, variability in alpha, the significance level.0042

So first things first, just a review of what the sample T really means.0048

So a lot of times people just memorize the T formula, it is you know the X bar minus mu over standard error but think about what this actually means.0056

So T equals X bar minus mu over the standard error.0070

And all right that is S sub X bar.0076

What this will end up giving you is this distance so the distance between your sample and your hypothesized mu.0079

And when you divided by standard error you get how many standard errors you need in order to get from bar to your mu.0087

So you get distance in terms of standard error.0097

So distance in terms of standard error.0102

And you want to think of in terms sort of like instead of using like feet or inches or number of friends, we get distance in the unit of standard error.0111

So whatever your standard error is for instance here that looks about right, because this is the normal0123

distribution that should be about 68% so that is the standard error.0132

Your T is how many of these you need in order to get to T.0139

So this might be like a T of 3 1/2, 3 1/2 standard errors away gets you from mu to your sample difference and so this is the case of the two sample t-test.0146

So independent samples are paired samples where we know the mu is zero.0164

So this is sort of the concept behind the T statistic.0169

Now here is the problem with this T statistic.0175

It is actually pretty sensitive to N.0181

So let us say you have a difference that is very, that is going to stay the same so a difference between, you know let us say 10 and 0.0185

So we have not that difference.0199

If you have a very very large N then your S becomes a lot skinnier.0202

And because of that your standard error is also going to shrink so the standard error shrinks as N shrinks.0212

And because of that, even though we have not changed anything about this mean, about the X bar or mu,0226

by shrinking our standard error we made our T quite large.0237

So we made our T like all of a sudden were 6 standard or errors away but I really have not changed the picture.0244

So that is actually a problem that T is so highly affected by N.0252

The problem with that is that you could artificially make a difference between means, look statistically significant by having a very large N.0258

So we need something that tells us this distance that is less affected by N and that is after effects size comes in.0268

So in effect size what we are doing is we want to know the distance in terms of something that is not so affected by N.0278

And in fact we are going to use the population standard deviation because let us think about T.0288

So that is X bar minus mu over standard error.0295

So this contrast that to looking at the distance in terms of the standard deviation of the population, what would that look like.0303

Well, we could actually derive the formula ourselves.0307

We want that distance in terms of you know number of inches or number problem correct or whatever the0321

raw score is over instead the standard error we would just use S or if you had it you would use Sigma.0328

So you could think of this as the estimated Sigma and this is like the real deal Sigma.0341

And that is what effect size is and effect size is often symbolized by the letters D and G.0349

D is reserved for when you have when you have Sigma, G is used for when you use S.0360

Now let us talk about the roles of effect size.0367

The nice thing about effect size is that the N does not matter as much whether you have a small sample or large sample the effect size stays similar.0373

In test statistics suggest T or Z, the N matters quite a bit and let us think again about why.0384

So the T statistic I have been writing at so far as over standard error but let us think about what standard error is.0396

Standard error is S divided by the square root of N, now as N gets bigger and bigger so let us think about N getting bigger.0406

This whole thing in the denominator, this whole idea this whole thing becomes smaller and smaller.0417

And when you divide a positive or negative or positive, if you divide some distance by a small number then0430

you end up getting a more extreme value, more extreme.0441

So by more extreme I mean way more positive, more positive ,more on the positive side or way more negative.0448

So the T statistic is very very sensitive to N so is the Z because Z the only difference is instead of S we use Sigma.0463

And so the same logic applies but for effect size T and G we do not divide by square root of N so in that way N does not really have as much.0474

Okay so one thing to remember is if you know Sigma use covens D, if you need to estimate the standard deviation from the sample S, you want to use hedges G.0488

Okay so now you know what effect size is and it is nice that it is not as affected by N but why do we need it?0500

Well effect size is what we use, the statistically used to interpret practical significant so for instance we 0839.8 might have some sort of very small difference between group 1 and group 2 so with the males and females0510

on some game or task, there is a very tiny difference like you know let us just say males are ahead by .0001 points.0527

And practically, it sort if does not matter but if you have a large enough effect size if you have a large0540

enough N you could get a small enough standard error that you can make that tiny difference seem like a0549

big deal and you can imagine that would be sort of odd situation.0556

We take a difference that sort of does not matter but then make a big deal out of it because of some fancy statistics we did.0565

Well, that effect size is not going to be affected by N and so that going to give you more straightforward0573

measure of is this difference big enough for sort of just care about.0580

It is not going to tell you whether it was significant or not based on hypothesis testing but it can give you0584

the idea of practical significant and here were using the modern term for significant as in important.0592

It will tell you a bit practical importance not statistical outlier nests, that is how it is telling you, it is talking0601

about just regular old practical importance and the way you can think about this is just thinking about it as is this different worth noticing.0614

Is that worth even doing statistics on?0623

The thing about hypothesis testing is that it could be deceiving, a very large sample size can lead to a0625

statistically significant one of these outlier differences that we really do not care about that just has no practical significant.0632

So here although we have been trying to talk about this again and again trying to sort of clarify that0641

statistically significant does not mean important it just means it lies outside of our expectation.0648

It is important to realize once again that statistical significance does not equal practical significant.0656

This is sort of talking about how important something is and this is just sort of saying, does it stand out?0663

Does our X bar our sample actually stand out?0672

Okay now let us move on to power.0679

What is power?0684

Well, how we really needs to go back to our understanding of the two types of errors.0685

Remember in hypothesis testing we can make an error in two different ways.0691

One is the false alarm error and we set that false alarm error rate by Alpha and the other kind of error is0695

this incorrect decision that we can make called the miss.0704

A miss is when we fail to reject the hypothesis, that the null hypothesis but we should, we really should.0708

And that is signified by beta, by the term beta.0717

Now when the null hypothesis is true then we can know if we already set our probability of making0725

incorrect decision, just like subtraction we can figure out our probability of making a correct decision so if 1225.5 our probability is .05 in making incorrect decision, the other possibility is that we may correct decision 95%, 1-.05.0739

In the same way when the null hypothesis is actually false we could figure out our probability of actually0756

making a correct decision by just subtracting our probability of making incorrect decision from one.0764

So this would be one minus beta.0772

In that way these two decisions that we make they add up to a probability of one and this 2 decisions that we can make add up to probability of one.0775

But in reality only one of these worlds is true that is why they both have a probability of 1.0787

We just have no idea whether this one is true or this one is true and anyone can never really say but that is the philosophical question.0794

So given this picture, power resides here and this quadrant is what we think as power.0802

Now power is just the idea given that the world is actually false, that this world we live in pretend we0811

ignore this part right so I am just, just ignore this entire world, given that this null hypothesis is false, what0824

is our probability of actually rejecting the null hypothesis and that is what we call power.0835

So think of this as the probability of rejecting null when the null is false.0843

So why do we need power, why do we need 1 – beta?0855

Well, here it is going to come back, those concepts come right back.0864

Remember the idea that you know sometimes we wanted to detect some sort of disease right and we0873

might give a test like for instance we want to know whether someone has HIV and so we give them a blood test to figure out, do they have HIV.0879

Now this test are not perfect and so there is some chance that they will be able to detect the disease and some chance that will make a mistake.0888

There is two ways that there is two ways of thinking about this prediction.0897

One is what we call a positive predictive, value we could think about what is the probability that someone has the disease for instance HIV given that they test positive?0903

Well this will help us know what is the chance that they actually have the disease once we know their test score.0916

In this world we know their test scores and we want to know what is the probability that they have the disease.0926

On the other hand we have what is called sensitivity.0932

Sensitivity thinks about the world in a slightly foot way.0936

Given that this person has the disease, has whatever disease such as HIV, one is the probability that they will actually test positive.0940

And said that at these two actually give us very different world.0950

In one world the given is that they have a positive test and what is the probability that they have the disease versus no decease.0954

In this scenario the given is very different.0967

The given is that they actually have the disease.0973

Given that what is the probability that they will test positive versus negative?0976

And so they are looking at this or they are looking at this.0983

Now power is basically the probability of getting a hit, the probability of rejecting that null hypothesis given 1637.9 that the null hypothesis is actually false so it is actually wrong.0988

Is this more like PPV, positive predictive value?1004

Or is it more like sensitivity.1010

Well let us think about this.1012

In this world there is this reality that the given reality is that this is false.1014

We need to reject it.1022

What is the probability that will actually be rejected?1027

So reject or fail to reject.1032

Well one way of thinking about this in a more, in the comparison is to consider, what is this thing that we1040

do not know in these two scenarios?1052

We do not really know if they actually have HIV.1055

We know their test we know that their test is either positive or negative and the test is uncertain but1059

whether they actually have HIV or not, that does not have uncertainty, it is just that we do not know what it is.1065

This is sort of like HIV in that way.1074

This is the reality so HIV is the reality and this, this is the test results.1078

This is also the reality and these are the results of hypothesis testing.1088

And so in that way this picture is much more like sensitivity.1101

And really when we apply the word sensitivity we see a whole new way of looking at power.1107

Power is the idea how sensitive is your hypothesis test when there really is something to detect, can it detect it?1116

When there really is HIV, can your test detect it?1125

When the null hypothesis really is false, can your test detect it?1129

That is the question that power is asking.1136

Okay if you calculate power is there nice little formula for?1139

Well power is more like the tables in the back of your book.1145

You cannot like calculate with like one simple straightforward formula.1148

There is actually more complex formula that does not both calculus but we can simulate power for a whole1153

bunch of different scenarios and those scenarios all depend on outline effect size and also variability in1161

sample size and because of that power is often found through simulation.1169

So I am not going to focus on calculating power, instead I am going to try to give you a conceptual understanding power.1174

Now often a desired level of power and sometimes you may be working with computer programs that might calculate power for you.1187

A different level of power that you want to shoot for is .8 or above but I want you to know how this power interact with all this things.1195

All of these things actually go into the calculation of power but I want you to know what is the conceptual level.1206

So how does Alpha or the significance level affect power, how does affect size, D or G affect power, how1212

does variability S or you know, S squared affect power and how to sample size affect power.1224

Okay so first thing is how does Alpha affect power?1234

Well here in this picture, I shown you 2 distribution.1241

You could think of this one is the null distribution and this one as the alternative distribution.1247

And noticed that both of these both of these distributions up here are exactly the same down here I just copied and pasted.1254

The only thing that is different is not their means or the actual distribution.1263

The only thing that is different is the cut off.1276

Since here, the cut off scores right here and this is the alpha, and hear the cutoff score has been moved sort of closer towards the population mean.1279

And now we have a huge Alpha.1297

So let us just assign some numbers here.1301

I am just guessing that maybe that looks like alpha equals .05 that something more used to see, but this looks like maybe Alpha equals let us say .15.1304

What happens when we increase our Alpha?1317

Our Alpha has gotten bigger, what happened, what happens to power?1323

Well it might be helpful to think about what Power might be?1326

In this picture, remember, power is the probability of rejecting the null hypothesis when the null hypothesis1331

is actually false and here we often reject when it is more extreme than the cutoff value when your X bar is1344

more extreme so these are the rejections of everything on this side.1358

All of this stuff is reject, reject the null.1362

And we want to look at the distribution where the null hypothesis is false a.k.a. the alternative hypothesis.1369

So really were looking at this big section right here so here this big section, that is power, one minus Beta1380

given that you could also figure out what beta is.1396

And beta is our error rate for misses.1400

When we fail to reject, fail to reject but the alternative hypothesis is true or the other way we could say it is the null hypothesis is false.1406

So what happens to power when Alpha becomes bigger?1421

Well, let us colour in power right here and it seems like there is more of this distribution that is been colored in than this.1430

So this part has been sort of added on, it used to be just this equals power but now we also have added on the section.1440

So as Alpha increases, power also increases.1453

And hopefully you can see that from this picture.1464

Now imagine moving Alpha out this way so decreasing Alpha.1467

If we decreased alpha then this power portion of the distribution that power part will become smaller so1472

the opposite, sort of the counterpoint to this is also true as Alpha decreases, the power also decreases.1483

But you might be asking yourself, then why cannot we just increase alpha so we could increase power right?1496

Well, remember what Alpha is, alpha is your false alarm rate.1511

So when you increase Alpha, you also increase your false alarm rate.1516

So at the same time if you increase your false alarm rate your increasing power.1521

And so this often is not a good way to increase power.1526

But you should still know, with the relationship is.1533

How about effect size, how does effect size affect power?1538

Well remember, effect size is really sort of a, you can think of it roughly as this distance between the X bar and the mu.1544

We are really looking at that distance in terms of standard deviation of the population.1555

How does effect size affect power?1561

Here I have drawn the same pictures, same cut off except I have moved this null, this alternative1564

distribution a little bit out to be a more extreme so that we now have a larger distance, larger distance.1574

And so this is a bigger effect size, bigger effect size so what happens when we increase the effect size and1587

we keep everything else constant, that the cut off, the null hypothesis, everything.1600

Well, let us colour in this, and colour in this.1605

Which of these two blue areas is larger.1612

Obviously this one.1616

This power is bigger than this power and it is because we have a larger effect size so another thing we have1618

learned is that larger effect size it leads to larger power so as you increase effect size you could increase power but here is the kicker.1627

Can you increase effect size?1646

Can you do anything about the effect size?1649

Is there anything you could do?1651

Not really.1655

Effect size is something that sort of out there in the data but you cannot actually do anything to make it1657

bigger but you should know that if you happen to have a larger effect size then you have more power than if your study of a small effect size.1663

Okay so how does variability and sample size affect power?1674

Now the reason I put these two things together is that remember, this distribution are S Toms, right?1686

And so the variability in a S Tom right is actually standard error and standard error is S divided by the square root of N.1696

So both variability and sample size will matter in power.1711

And so here I want to show you how.1720

Okay so here I have drawn the same means of the population of the S Toms and remember here we have1723

the null hypothesis and the alternative hypothesis distribution.1733

I have drawn the same pictures down here and I kept the same Alpha about .05.1739

So I had to move the cut off a little just so that I could color in .05 but something has changed and that is this.1749

This a lot skinnier than this is, that is less variability so that S Tom has decreased in variability.1760

So here standard error has decreased so we have sharper S Toms.1772

Still normally distributed, just sharper.1786

And so when we look at these skinnier distribution let us look at the consequences for power.1790

Here lets color in power and let us color in power right here and it also helps to see what beta is.1798

So here we have a quite a large beta and here we have a tiny beta.1810

And so that makes you realize that the one minus Beta appear the power here is larger than the one minus Beta down here.1815

This is smaller than the 1 – beta down here because remember were talking about proportions.1832

This whole thing add up to 1, now this might look smaller to you, the whole thing adds up to one.1849

If this is a really small proportion so let us do a number on it, that was less than .05.1855

Let us go on .02.1861

This looks bigger than .05 so let us go on .08.1863

Then 1 - Beta here would be 92% and 1 - Beta here would be 98% so this is a larger power than this.1869

So one thing we have seen is our standard error decreases so it is decreasing then power increases so this is what we call a negative relationship.1879

As one goes down the other goes up and vice versa as standard error increases as these distribution1899

become fatter and fatter then power will decrease overall the opposite way.1908

Now because we already know this about standard error we could actually say something about sample1913

size because sample size actually has the opposite it also has a negative relationship with standard error1921

and sample size go get bigger and bigger and bigger standard error gets smaller and smaller and smaller1929

and so sample size actually have a positive relationship with power so as sample size increases and1936

therefore standard error decreases, power increases.1947

And so we could figure that out just by reasoning through what standard error really mean.1954

Okay so how do we increase power because often times your power or sensitivity is really a good thing.1965

We want to be able to have experiments and studies that have a lot of power that would be a good hypothesis testing adventure to embark on.1976

How do we actually increase it.1987

Well can we just do this by changing Alpha?1989

Well the problem with this is that you get some consequences namely that falls alarms increase.1994

So if you increase power with this strategy you are also going to increase false alarm, that is very dangerous so that is not something we want to do.2010

That is type 1 error so that is something we do not want to do.2020

So you do not want to change power by changing Alpha although that is something under our control.2023

Now we could try to change effect size but because of effect size is something that is already sort of true in2029

the world right like what we have to do to mess with standard error of the standard deviation of the2039

population, we cannot mess with that so this is actually something that is impossible to do.2045

So that is one thing that we wish we could do but cannot do anything about.2052

Can we change the variability in our sample, can we change the variability?2067

Indirectly, we can.2072

There is really one way to be able to change standard error.2075

Can we do this by changing the standard deviation of the population?2081

No, we cannot do that, that is out of our control.2085

But we can change N.2090

We can collect more data instead of having 40 subjects or cases in our study, we can have 80.2093

And in that way we can increase our power and so really the one thing that sort of one tool that sort of2102

available to us as researchers in order to affect power is really affecting sample size.2110

None of these other things are really that appealing to us.2116

We cannot change population variability, we cannot change effect size and if we change Alpha then that is a dangerous option.2120

And so what we have left here is affecting sample size.2133

Now let us go on to some examples.2139

Statistical test is designed with a significance level of .05 sample size of 100.2144

As similar test of the same null hypothesis is designed with a significant level of .1 and a sample size of 100.2149

If the null hypothesis is false which test has greater power?2160

Okay so let us think about this.2165

Here we have a situation one test one where Alpha equals .05.2168

Test 2 the other test, alpha = .10 so here Alpha is larger.2178

Remember alpha is moving that critical test statistic so we have taken this and let us have this Alpha right2190

here and what we do is we moved it over, moved it over here, well not that far but just so you can get the idea.2205

And now our Alpha is much bigger but what we see is that our beta, our 1 - Beta has also gotten a lot bigger.2217

So here we see that power increases but we should also note that now we have a higher tolerance for false2231

alarms so we will also have more false alarm, will have more times when we reject the null period so we2244

will reject the null lots of time sometimes will be right, sometimes will be wrong, both of this things increase.2251

Example 2.2258

Suppose the medical researcher was to test the claim of the pharmaceutical company that the mean number of side effects per patient for new drug is 6.2261

The researcher is pretty sure the true number of side effects is between 8 and 10 so there like2270

pharmaceutical company not telling the whole truth.2277

Shows a random sample of patients reporting side effects and chooses the 5% level of significance so Alpha equals .05.2281

Is the power of the test larger is the true number of side effects is 8 or 10.2288

So let us sort of think about okay what is the question really asking and then explain.2295

So is the true number of side effects is 8 or 10 is really talking about your mu?2302

And actually, here we are talking about the alternative mu because the null mu is probably going to be six.2309

So here is the null hypothesis.2325

The null hypothesis is that the pharmaceutical company is telling the truth.2330

So the null hypothesis mu is six.2334

Now, if the alternative mu is 8, it will be, maybe about here but if the real alternative population is actually2337

a 10, so the other alternative, a 10, it is way out here.2350

And which of these scenarios is the power larger.2359

Well even if we set a very conservative critical test statistic, here is our power for 8 as is the true number of2365

side effects but here is the power almost 100% for 10 being the true number of side effects and remember2382

I am just trying these with just some standard error I do not care what it is just have to be the same across all of them.2395

And so here we see that wow, it is way out farther, more of this is going to be covered when we reject the null.2402

And so we see that the power is larger, is the true number of side effects is 10.2413

And the reason for that is because this is really a question about effect size.2420

The true certain distance between our null hypothesis distribution and our alternative hypothesis distribution.2428

We know that as effect size goes up power also goes up easier to detect but we cannot do anything we cannot actually make effect size bigger.2440

Example 3.2455

Why are both the Z and T statistic affected by N while Colens D and hedges G are not then what do the Z,2458

T, D and G all have in common and finally, what commonality does Z and D share.2469

What commonality does T and G share?2478

Well, I am going to draw this as sort of Ben diagram.2481

So let me draw Z here and here, I will drop T and then here I will draw D and it is going to get crazy, here I will draw G.2484

Now, if it helps, you might want to think about what these guys mean over the actual population, standard2511

deviation over the standard error derived from the population standard deviation.2523

And here we have standard error derived from the derived from the estimated population standard2538

deviation whereas in D we have the distance, same distance, here just divided by Sigma, here we have the same distance divided by S.2547

Okay so why are both the Z and T statistic affected by N while Colens D and Hedges G are not?2570

Well, the thing that these two have in common is that these are about standard error and standard error is2579

either Sigma divided by square root of N or S divided by square root of N and it is this dividing by square2587

root of N that makes these two so affected by N.2602

And so it is really because they are distances in terms of standard error.2607

So when do the Z, T, D and G all have in common so that is that is the little guy right here, what do they all have in common?2614

Well they all have this thing in common.2627

So they are all about the distance between sample and population.2629

So it is all about that distance.2641

Some of them are in terms of standard error and some of them are in terms of population standard deviation.2644

So what commonality does Z and D share.2651

Well that going to be right in here.2656

What do they have in common, they both rely on actually having Sigma.2658

T and G both rely only on the sample estimate of the population standard deviation.2663

So looks a little messy but hopefully this makes a little more sense.2671

Thanks for using educator.com for effect size and power.2676

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