Insights into the concepts of hypothesis testing (Part 9)

Consequences of Hypothesis Testing

In continuation with yesterday’s example, if hypothesis testing is based on well designed experiments aimed at maximizing the information with minimum background noise and complemented by a representative sampling scheme, then we are most likely to make correct decisions. Among four possible consequences listed below, only one will take place.

Null Hypothesis: No increment in SPM of 2017 to 2016

Alternative Hypothesis: There is an increment in SPM of 2017 to 2016

Correct Decision: Accepting a true null hypothesis. This implies that we correctly conclude that there is no increment in SPM in 2017 in comparison to 2016.

Correct Decision: Rejecting a false null hypothesis. This implies that we correctly conclude that there has been an increment in SPM of 2017 in comparison to 2016.

Error I: Type I Error: Rejecting a true null hypothesis and thus falsely concluding that SPM in 2017 is significantly more than 2016. Consequences of this error will be very expensive (time and money) for the government, so the government will be wrongly forced to take measures to control pollution which is beneficial to the public.

Error II: Type II Error: Accepting a false null hypothesis and thus falsely concluding that there is no change in SPM of 2017 in comparison to 2016. Consequences of this conclusion will be drastic for the public as it will have adverse impact on their health.