What is hypothesis testing?

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Hypothesis testing is a fundamental statistical method used to make decisions or inferences about a population based on sample data. It helps determine whether there is enough evidence to support a specific claim or assumption.

Key Concepts:

1. Null Hypothesis (H₀):
   This is the default assumption, usually stating that there is no effect or no difference. For example, “a new drug has no effect on blood pressure.”

2. Alternative Hypothesis (H₁ or Ha):
   This represents the claim you want to test, suggesting there is an effect or a difference. For example, “the new drug lowers blood pressure.”

3. Test Statistic:
   A numerical value calculated from the sample data that measures how much the observed data deviates from what is expected under the null hypothesis.

4. P-value:
   The probability of observing the test results, or more extreme, assuming the null hypothesis is true. A small p-value (usually < 0.05) indicates strong evidence against the null hypothesis.

5. Significance Level (α):
   A threshold probability set before the test (commonly 0.05), below which the null hypothesis is rejected.

6. Decision:

   • If p-value ≤ α → Reject H₀ (supporting H₁)

   • If p-value > α → Fail to reject H₀ (insufficient evidence to support H₁)

Summary:

Hypothesis testing is a systematic method to evaluate claims about a population using sample data. It balances the risk of making errors while providing a statistical framework for decision-making in research, business, and science.

Read More :

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Define correlation vs. causation.

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