Insights into the concepts of hypothesis testing (Part 2)

As mentioned on yesterday’s BLOG in Hypothesis Testing, null hypothesis is interested in maintaining the “Status Quo”. Alternative hypothesis is the opposite of null hypothesis.

We don’t know the consequences of our decisions. We don’t have the luxury of knowing each and every unit of the population so we have to be satisfied with the sample. But results obtained from this sample should hold true for the entire population. Because of this generalization of results we run the risk of committing two types or errors. Either we can reject a true null hypothesis that is called Type I error OR we can accept a false null hypothesis that is called Type II error. Falsely rejecting a true null hypothesis in favor of alternative hypothesis disturbs our “Status Quo”. So committing type I error lands us in a worst off scenario.

For example: A person is looking for greener pastures and so is in look out for a new job.

Null Hypothesis: Current job is good   (maintaining the status quo)

Alternative Hypothesis: The new job is good (opposite of null hypothesis)

Consequence of Type I error

Leaving the old job and being unsatisfied (due to poor wages and bad working conditions) with the new job. This error disturbs our “Status Quo” and moves us to a worst scenario.

Consequence of Type II error

Remaining in the old job and remaining unsatisfied. This error is not so serious as Type I error as we are not pushed below from our “Status Quo” position.