Module 9: Hypothesis Testing
The "effect" of a particular factor on some health outcome can be described as a parameter. Statistical hypothesis testing begins with a probability model assuming there is no effect or a null hypothesis (H0) and deciding whether there is sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis (H1). Think of it like a legal trial; innocent until proven guilty.
One-sided Hypothesis: H0: θ = θ0 vs H1: θ > θ0 (or θ < θ0)
Two sided Hypothesis: H0: θ = θ0 vs H1: θ != θ0
Errors
Type I error or false positive is rejecting H0 when it is true. α represents the probability of this error (typically set at .05)
Type II error or false negative is failing to reject the null when it is false. Probability represented by β.