Introduction to hypothesis testing
Hypothesis testing is an act in statistics in which an analyst tests an assumption concerning a population parameter. The methodology employed by the analyst basically depends on the nature of the data used and the reason for the analysis.
Hypothesis testing is used to evaluate the plausibility of a hypothesis by using sample data. Such data may come from a more significant population or a data-generating process.
Understanding Hypothesis Testing
In hypothesis testing, an analyst will test a statistical sample to provide evidence on the plausibility of the null hypothesis.
Statistical analysts test a hypothesis by surveying and examining a random sample of the population being studied. All the analysts make use of a random population sample to test two hypotheses, i.e., the null hypothesis and the alternative hypothesis.
The null hypothesis is normally a hypothesis of equality between the population parameters, e.g., a null hypothesis may declare that the population mean return is equivalent to zero.
The alternative hypothesis is completely the opposite of a null hypothesis (e.g., the population mean return is not equivalent to zero). Therefore, they are mutually exclusive, and only one can be true. Yet, one of the two hypotheses will always be true.
4 Steps Of Hypothesis Testing
All hypothesis are tested using a four step process:
The initial step is for the analyst to declare the two hypotheses so that only one can be right.
The following step is to formulate an analysis plan, which outlines how the data will be assessed.
The third step is to work out the plan and then physically analyze the sample data.
The fourth and ultimate step is analyzing the results and either reject the null hypothesis or state that the null hypothesis is credible, given the data.
Importance Of Hypothesis Testing
According to the San Jose State University Statistics Department, hypothesis testing is essential in statistics. It is how you determine if something happened, or if particular treatments have positive effects, or if groups differ or if one variable predicts another.
In short, you want to prove if your data is statistically meaningful and unlikely to have occurred by chance only. In essence, then, a hypothesis test is known as a test of significance.
