What is Hypothesis Testing? Hypothesis Testing Explained
Hypothesis testing is a statistical procedure used to make inferences or draw conclusions about a population based on sample data. It involves formulating a null hypothesis and an alternative hypothesis, collecting data, and applying statistical tests to determine the likelihood of the observed data given the null hypothesis.
The basic steps involved in hypothesis testing are as follows:
State the Null and Alternative Hypotheses: The null hypothesis (H0) is the statement that there is no significant difference or relationship between variables or no effect of a treatment. The alternative hypothesis (Ha or H1) is the statement that contradicts the null hypothesis and suggests that there is a significant difference or relationship.
Select the Significance Level: The significance level (often denoted as α) is the probability of rejecting the null hypothesis when it is actually true. Commonly used significance levels are 0.05 (5%) or 0.01 (1%). The significance level determines the threshold for considering the evidence against the null hypothesis as statistically significant.
Collect and Analyze Data: Collect a sample of data relevant to the hypothesis being tested. Apply appropriate statistical tests based on the type of data and the research question. The choice of statistical test depends on factors such as the nature of the variables, sample size, and assumptions about the data distribution.
Calculate Test Statistic: The test statistic is a numerical summary derived from the sample data that measures the degree of agreement or disagreement between the data and the null hypothesis. The specific test statistic depends on the hypothesis being tested and the chosen statistical test.
Determine the Critical Region: The critical region is the range of values of the test statistic for which the null hypothesis is rejected. It is determined by the chosen significance level and the distribution of the test statistic under the null hypothesis. If the calculated test statistic falls within the critical region, the null hypothesis is rejected in favor of the alternative hypothesis.
Compare the Test Statistic with Critical Values: Compare the calculated test statistic with the critical values associated with the chosen significance level. If the test statistic exceeds the critical value(s), the null hypothesis is rejected. Otherwise, if the test statistic falls within the non-critical region, the null hypothesis is not rejected.
Draw Conclusions: Based on the results of the statistical test, draw conclusions regarding the hypotheses being tested. If the null hypothesis is rejected, it suggests evidence in favor of the alternative hypothesis. If the null hypothesis is not rejected, there is insufficient evidence to support the alternative hypothesis.
It’s important to note that hypothesis testing provides evidence against or in favor of the null hypothesis but cannot prove the alternative hypothesis to be true. The conclusions drawn from hypothesis testing are based on probabilities and the strength of evidence in the data.
Hypothesis testing is widely used in various fields, including social sciences, medicine, economics, and engineering, to evaluate theories, test research hypotheses, and make data-driven decisions.
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