Zach’s facts have been extracted from the book to remind you of the key concepts you and Zach have learned in each chapter.
Zach's Facts 10.1 NHST
- Null hypothesis significance testing (NHST) is a widespread method for assessing scientific theories. It uses two competing hypotheses: one says that an effect exists (the alternative hypothesis) and the other says that an effect doesn’t exist (the null hypothesis). You compute a test statistic that represents the alternative hypothesis and calculate the probability that you would get a value as big as the one you have if the null hypothesis were true. If this probability is less than 0.05 scientists reject the idea that there is no effect and conclude that they have a statistically significant finding. If the probability is greater than 0.05 they do not reject the idea that there is no effect, and conclude that they have a non-significant finding.
- You can make two types of error: (1) believe that there is an effect when, in reality, there isn’t (a Type I error); and (2) believe that there is not an effect when, in reality, there is (a Type II error).
- The power of a statistical test is the probability that it will find an effect when one actually exists.
- The significance of a test statistic is directly linked to the sample size. The same effect will have different p-values in different-sized samples: small differences can be deemed ‘significant’ in large samples, and large effects might be deemed ‘non-significant’ in small samples.