You have to learn this logic!
The Simple Steps of Hypothesis Testing:

1. You come up with your hypothesis (for example - college students sleep less than other folks).
   
   
2. You generate a sample (pick a set of college students).
   
   
3. You calculate your summary statistics (for example, the mean and SD of number of hours that college students sleep per night).
   
   
4. You determine the statistical test that will compare your summary statistic against the value determined by your Null Hypothesis. (You would use the single sample t-test for college students’ sleep.)
   
   
5. You calculate the test statistic using your summary statistics. The formula for the test statistics is different for each type of test but the basic concept is the same. You calculate how far your sample is from the Null Hypothesis taking into account that sample values of a statistic vary by chance when smaller samples are taken from a larger population. The SE tells us how much they vary.
   
   
6. You derive the appropriate sampling distribution - or refer to one already listed in the tables in your statistics book. Your computer program can also give you this information.
   
   
7. You choose the cut-off value on your sampling distribution that tells you that your sample statistic is very far from the Null Hypothesis and thus not likely. We call this cut-off value our alpha level or significance level (more about alpha later).
   
   
8. You decide whether to reject the Null Hypothesis or fail to reject the null. You do this by comparing your test statistic to the cut-off value.
   
   
9. You draw your conclusion. If you reject the Null Hypothesis, you say that your result is statistically significant. This simply means that it did not happen by luck or chance. If you fail to reject the null, you conclude that you did not find an effect or difference in this study.