Since there are many facets to hypothesis testing, we start with the example we refer to throughout this guide.
In statistics terminology, the students in the study are the sample and the larger group they represent (i.e., all statistics students on a graduate management degree) is called the population.
Given that the sample of statistics students in the study are representative of a larger population of statistics students, you can use hypothesis testing to understand whether any differences or effects discovered in the study exist in the population.
The former process was advantageous in the past when only tables of test statistics at common probability thresholds were available.
It allowed a decision to be made without the calculation of a probability.
However, this is generally of only limited appeal because the conclusions could only apply to students in this study.
However, if those students were representative of all statistics students on a graduate management degree, the study would have wider appeal.
The first type of error occurs when the null hypothesis is wrongly rejected.
The second type of error occurs when the null hypothesis is wrongly not rejected.
In layman's terms, hypothesis testing is used to establish whether a research hypothesis extends beyond those individuals examined in a single study.
Another example could be taking a sample of 200 breast cancer sufferers in order to test a new drug that is designed to eradicate this type of cancer.