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Introduction: Hypothesis Tests

• The null hypothesis is a claim about a parameter that is assumed true until proven otherwise. If the null hypothesis is false, the data supports the alternative hypothesis.

• When making a decision, there are two types of errors. A Type I error is when a true null hypothesis is rejected. A Type II error is when a false null hypothesis is not rejected.

• A p-value is the chance that we obtained what we observed or something more extreme in the direction of the alternative hypothesis if the null hypothesis is true.