Outline the basic steps of hypothesis testing.

Outline the basic steps of hypothesis testing.

• A. State H0 and H1, choose α, compute test statistic, determine p-value or critical region, compare to α, and draw conclusion.
• B. Collect data and calculate confidence intervals.
• C. Formulate a hypothesis after analyzing data.
• D. Decide sample size based on desired power.

Answer: (A)

Hypothesis testing is about weighing evidence from data against a baseline claim. Start by clearly stating two competing statements: a null hypothesis (a statement of no effect or status quo) and an alternative hypothesis (the claim you want to investigate). Then choose a significance level, α, which sets how strong the evidence must be to reject the baseline. Next, compute a test statistic from your sample data that measures how far the observed result is from what the null hypothesis would predict, using the appropriate distribution (for means, z or t; for variances, chi-square; etc.). After that, determine how extreme the result is under the null: either compute a p-value, which is the probability of seeing data as extreme or more under H0, or identify the critical region with boundaries tied to α. Finally, compare the p-value to α or see if the test statistic falls into the critical region. If the result is sufficiently extreme (p-value ≤ α or statistic in the critical region), you reject the null in favor of the alternative; otherwise you do not reject the null and conclude there isn’t enough evidence to support the alternative. The process emphasizes controlled decision making: α bounds the chance of a false positive, and the steps ensure the conclusion follows from the data in a formal way. In practice you can interpret the conclusion in context of the research question and, if helpful, complement it with confidence intervals that estimate the parameter of interest.

Hypothesis testing is about weighing evidence from data against a baseline claim. Start by clearly stating two competing statements: a null hypothesis (a statement of no effect or status quo) and an alternative hypothesis (the claim you want to investigate). Then choose a significance level, α, which sets how strong the evidence must be to reject the baseline. Next, compute a test statistic from your sample data that measures how far the observed result is from what the null hypothesis would predict, using the appropriate distribution (for means, z or t; for variances, chi-square; etc.). After that, determine how extreme the result is under the null: either compute a p-value, which is the probability of seeing data as extreme or more under H0, or identify the critical region with boundaries tied to α. Finally, compare the p-value to α or see if the test statistic falls into the critical region. If the result is sufficiently extreme (p-value ≤ α or statistic in the critical region), you reject the null in favor of the alternative; otherwise you do not reject the null and conclude there isn’t enough evidence to support the alternative.

The process emphasizes controlled decision making: α bounds the chance of a false positive, and the steps ensure the conclusion follows from the data in a formal way. In practice you can interpret the conclusion in context of the research question and, if helpful, complement it with confidence intervals that estimate the parameter of interest.