• We always test a null hypothesis against an alternative/research hypothesis.
  
  
• If a sample is close to null, we conclude that nothing happened in the study. If a sample is far away or different from the null, we reject the null hypothesis and conclude that something happened.
  
  
• The logic of hypothesis testing is counterintuitive (or backwards). We test whether nothing happened (our sample value is close to the null) in order to conclude that something happened.
  
  
• There are two types of error in hypothesis testing (Type I and Type II). Type I errors occurs when we conclude that there is a difference when there is not. Type II errors occur when we conclude that there is no difference when there is.
  
  
• Statistical power is the probability of correctly detecting a difference between the sample and the population. In any study, we want to maximize the probability of detecting a true difference.

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