What is hypothesis testing?

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Hypothesis testing is a statistical method used to make decisions or inferences about a population based on sample data. It helps determine whether there is enough evidence to reject a null hypothesis (H₀) in favor of an alternative hypothesis (H₁).

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Steps in Hypothesis Testing:

1. State the Hypotheses

   • Null Hypothesis (H₀): Assumes no effect or difference.

   • Alternative Hypothesis (H₁): Assumes there is an effect or difference.

2. Choose a Significance Level (α)

   • Common choices: 0.05, 0.01

   • It represents the risk of rejecting H₀ when it is actually true (Type I error).

3. Select a Test Statistic

   • Depends on the type of data and sample size (e.g., z-test, t-test).

4. Calculate the Test Statistic and p-value

   • Use sample data to compute the test statistic and corresponding p-value.

5. Make a Decision

   • If p-value ≤ α, reject H₀ (support H₁).

   • If p-value > α, fail to reject H₀ (not enough evidence).

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Example:

You want to test if a new teaching method improves student scores.

• H₀: New method has no effect.

• H₁: New method improves scores.
  If p-value = 0.02 and α = 0.05 → reject H₀ → conclude the new method is effective.

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Importance:

Hypothesis testing is widely used in research, business, medicine, and social sciences to make data-driven decisions and validate assumptions.

In short, it provides a structured framework to evaluate assumptions and support conclusions with statistical evidence.

Read More:

What is the difference between population and sample?

What is p-value and its significance?

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