Null hypothesis ( H 0) is a population statement that asserts that there is no significant difference between two variables while the alternative hypothesis ( Ha) indicate that there is difference between two variables in either way i.e. smaller or greater. Null hypothesis is usually the one that the researcher seeks to disapprove. To illustrate this, I will use my personal example.
In my example, I want to test for social media effect to my working efficiency as a student. To do this, I intend to stop using social media for one month and observe whether there is change or not to the proportion of work that I will complete. In this case, we will assume that µo is the proportion of work completed at the start of the month while µ is the proportion completed after quitting social media. My claim is that stopping the use of social media will increase the proportion of work I complete. Based on this,
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Null hypothesis Ho: µ ≤ µo (meaning no desired change was observed; remained constant or decreased)
Alternative hypothesis Ha: µ > µo (meaning final outcome is greater than previous; significant difference)
Errors in Such a Scenario
In my example, the two types of errors are likely to occur. First, if the proportion of work does not increase and I reject the null hypothesis Ho: µ ≤ µo, I will have committed type I error. On the other hand, if the proportion of work increases after quitting social media and I accept the null hypothesis, then I commit type II error.
