Insights into the concepts of hypothesis testing (Part 6)

Choice of appropriate null and alternative hypothesis

Null hypothesis is the hypothesis of no difference. Alternative hypothesis is the opposite of null hypothesis. The choice of an appropriate null and alternative hypothesis always poses as a cause of concern among researchers. Null Hypothesis supports the status quo of the scenario of study. Alternative hypothesis tries to endorse the result obtained from sample data. We want to use sample data as evidence and reject the null hypothesis in favor of alternative hypothesis. For example if the sample data shows an increment in air pollution in comparison to last year the alternative hypothesis should reflect this trend. Then the null hypothesis will be the opposite of alternative hypothesis which is also the “Status Quo”. How is this status quo known to us? This is known through previous studies or through previous studies or through no difference assumptions or through the opposite of alternative hypothesis.

For example: We want to test whether the level of air pollution in terms of Suspended Particulate Matter (SPM) for the year 2017 is higher than that of 2016. We analyze this on the basis daily data of SPM,  collected in March – April (dry seasons) at a specific time say between 12:00 hours – 14:00 hour, for year 2017 and 2016.

What should be the Null hypothesis and Alternative hypothesis? What will be the consequence of Type I Error and Type II Error? This will be discussed tomorrow