Formulate Hypotheses: Choose a Test Statistic: Determine the Test Distribution: Collect Data and Compute the Test Statistic: Collect the relevant data and calculate the value of the chosen test statistic. Calculate the P-value: Using the test statistic and its distribution, calculate the p-value, which represents the probability of observing a test statistic as extreme or more extreme than the one computed, assuming the null hypothesis is true. For one-tailed tests: The p-value is the probability in the tail(s) of the distribution corresponding to the alternative hypothesis. If the alternative hypothesis is directional (e.g., "greater than" or "less than"), the p-value is calculated from the corresponding tail. If the alternative hypothesis is non-directional (e.g., "not equal to"), the p-value is calculated from both tails. For two-tailed tests: The p-value is calculated from both tails, representing extreme values in either direction. Compare the P-value with the Significance Level: Compare the obtained p-value with a predetermined significance level (α) to make a decision. The significance level is typically set at 0.05 (5%) or a more stringent value. If the p-value is less than or equal to the significance level, you reject the null hypothesis in favor of the alternative hypothesis. If the p-value is greater than the significance level, you fail to reject the null hypothesis. More on reddit.com

The null is ALWAYS the opposite of what you want to prove. It is related to modus tollens. If A then B and Not B therefore not A. More on reddit.com