Differences Between Null And Alternative Hypotheses

Null and alternative hypotheses are commonly used for testing in such applications as SPSS, which proposes different formulas for data calculation. The difference in the results over time does not always show the difference in the actual data statistics. Understanding the influencers on both types of hypotheses is important to provide the most realistic testing results. For example, while the null hypothesis does not affect the population, the alternative hypothesis considers the population as the key part of the testing (Gelman, 2017). Even though the results for null and alternative hypotheses can be the same, the difference may occur during the failure to reject the null hypothesis.

At the beginning of testing, the null hypothesis is considered correct unless researchers do not have evidence that states a different argument. When the test is executed, the p-value may be less, more, or equal to the significance level. The null hypothesis can be rejected when the p-value is less or equal to the significance level. In this case, there is no difference between null and alternative hypotheses. However, if the null hypothesis is greater than the significance level, failure in rejection may occur, and the difference between the two hypotheses appears. The alternative hypothesis should be supported with other evidence based on the testing of variables with the use of such techniques as sample T-test.

For example, the worker receiving $90,000 a year shows the testing result of the null hypothesis. The alternative hypothesis would state that the workers do not receive $90,000 a year. If the null hypothesis is rejected in this case, researchers will not have enough evidence to prove the alternative hypothesis is correct and more testing should be conducted. In conclusion, both hypotheses are dependent, and rejecting null hypotheses might cause difficulties in calculating alternative results.


Gelman, A. (2017). The failure of null hypothesis significance testing when studying incremental changes, and what to do about it. Personality and Social Psychology Bulletin, 44(1), 16-23.