Hypothesis Testing Checklist for Singapore JC2 Math Exams

Hypothesis Testing Checklist for Singapore JC2 Math Exams

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Frequently Asked Questions

The first step is to clearly state the null and alternative hypotheses. This involves defining the population parameter you are testing and setting up the opposing statements.
The choice of test statistic depends on the population parameter being tested (mean, proportion), the sample size, and whether the population variance is known. Common tests include the z-test, t-test, and chi-square test.
The significance level (alpha) is the probability of rejecting the null hypothesis when it is true. It is typically chosen as 0.05, 0.01, or 0.10, depending on the desired level of confidence.
The p-value is the probability of observing a test statistic as extreme as, or more extreme than, the one calculated from your sample, assuming the null hypothesis is true. It can be found using statistical tables or calculators.
Compare the p-value to the significance level (alpha). If the p-value is less than or equal to alpha, reject the null hypothesis. Otherwise, do not reject the null hypothesis.
Rejecting the null hypothesis means there is sufficient evidence to support the alternative hypothesis. In the context of a problem, this means concluding that there is a statistically significant effect or difference.
Type I error (rejecting a true null hypothesis) and Type II error (failing to reject a false null hypothesis). To minimize these errors, choose an appropriate significance level and ensure sufficient sample size.
State whether you reject or do not reject the null hypothesis. Then, interpret this result in the context of the problem, clearly stating whether there is sufficient evidence to support the alternative hypothesis.