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A clear guide explaining hypothesis testing, its steps, and its role in research and analytics.
Hypothesis testing is a statistical method used to make inferences about a population based on sample data. It helps determine whether there is enough evidence to support or reject a proposed assumption.
Definition
Hypothesis testing is the process of evaluating assumptions about population parameters using statistical techniques and sample data.
The process begins with a null hypothesis (H₀), which represents the default assumption, and an alternative hypothesis (H₁), which represents the claim being tested. Statistical tests evaluate whether observed data is sufficiently unlikely under the null hypothesis.
Researchers calculate a test statistic and compare it to a critical value or use a p-value to decide whether to reject the null hypothesis. The choice of test depends on data type, distribution, and research design.
Hypothesis testing is widely used in economics, finance, medicine, marketing, and social sciences to support evidence-based decisions.
A company tests whether a new marketing campaign increases average sales. The null hypothesis assumes no change, while the alternative hypothesis assumes higher sales after the campaign.
Hypothesis testing is important because it:
It is the default assumption that there is no effect or difference.
The probability of observing results as extreme as the sample, assuming the null hypothesis is true.
It provides evidence in favor of H₁, but not absolute proof.