Basics of Hypothesis Testing
Almost 99% of the times we don’t have access to the population data and we are expected to make estimations about the population parameters from a sample of the population data.
So what is Hypothesis Testing?
There are ways to check if your hypothesis has any truth to it, and if the hypothesis is true then apply it to the population parameters. This is called hypothesis testing. The goal is to determine whether there is enough evidence to infer that the hypothesis about the population parameter is true. In hypothesis testing, we confirm our assumptions about the population based on sample data.
Difference between Inferential Statistics & Hypothesis Testing
Inferential statistics is used to find the mean of a population parameter when you have no initial number to start with. So, you start with the sampling activity and find out the sample mean. Then, you estimate the population mean from the sample mean using the confidence interval.
Hypothesis testing is used to confirm your conclusion (or hypothesis) about the population mean (which you know from EDA or your intuition). Through hypothesis testing, you can determine whether there is enough evidence to conclude if the hypothesis about a population parameter is true or not.
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Hypothesis Testing starts with the formulation of two hypotheses:
- Null hypothesis (H₀): The status quo
- Alternate hypothesis (H₁): The challenge to the status quo
Example: Suppose a patient has been tested for a disease. The Null Hypothesis would be that the Patient has the disease. And the Alternate Hypothesis would be that the patient does not have the disease.
Either of your above Hypothesis might be True. But remember:
- You can only Reject the Null Hypothesis when your Alternate hypothesis is True. There is no such thing as Accepting the Alternate Hypothesis.
- You can only Fail to Reject the Null Hypothesis when your Null Hypothesis is True. There is no such thing as Accepting the Null Hypothesis.
You can use the following rule to formulate the null and alternate hypotheses:
The null hypothesis always has the following signs: ‘=’ OR ‘≤’ OR ‘≥’
The alternate hypothesis always has the following signs: ‘≠’ OR ‘>’ OR ‘<’
So, by now I am sure you are clear about the basic concepts in Hypothesis Testing and the intuition behind it. In the next Article, I will be discussing about the Methods of performing Hypothesis Testing.
