Hypothesis Testing: Explained in simple words

Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used and the reason for the analysis.
Hypothesis testing is a form of statistical inference that uses data from a sample to draw conclusions about a population parameter or a population probability distribution.
For example, assume that a radio station selects the music it plays based on the assumption that the average age of its listening audience is 30 years.
To determine whether this assumption is valid, a hypothesis test could be conducted with the null hypothesis given as H0: μ = 30 and the alternative hypothesis given as Ha: μ ≠ 30.
Based on a sample of individuals from the listening audience, the sample mean age, x̄, can be computed and used to determine whether there is sufficient statistical evidence to reject H0
All hypotheses are tested using a four-step process:
The first step is for the analyst to develop or formulate hypotheses, that is, the two hypotheses, null and alternative.
The next step is to formulate an analysis plan/statistical analysis plan, which outlines how the data will be evaluated/analyzed.
The third step is to analyze the sample data.
The fourth and final step is to either reject the null hypothesis, or state that the null hypothesis is plausible, given the data.

[@akpashtoon1981]

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