AWS Certified Machine Learning – SpecialtyExploratory Data AnalysisHard
A data scientist is performing a rigorous hypothesis test on a new advertising campaign's effectiveness. They have a control group and an experimental group, and the outcome variable ('ConversionRate') is continuous and approximately normally distributed. After running the test, they obtain a p-value of 0.03. The chosen significance level (alpha) for this study is 0.05. Which of the following is the correct conclusion?
- AReject the null hypothesis, as 0.03 < 0.05.
- BAccept the null hypothesis, as 0.03 < 0.05.
- CThe results are inconclusive, as 0.03 is very close to 0.05.
- DFail to reject the null hypothesis, as 0.03 < 0.05.
Show answer & explanationAnswer & explanation
Correct answer: A. Reject the null hypothesis, as 0.03 < 0.05.
In hypothesis testing, if the p-value is less than the chosen significance level (alpha), the null hypothesis is rejected. Here, 0.03 (p-value) is less than 0.05 (alpha), indicating that there is sufficient statistical evidence to reject the null hypothesis and conclude a significant difference.
Why the other options are wrong
- B. Accepting the null hypothesis is incorrect; we reject it when p < alpha.
- C. The conclusion is not inconclusive; the rule for rejection is clear.
- D. Failing to reject the null hypothesis is incorrect; we reject it when p < alpha.
P-value Interpretation
The p-value is the probability of observing a test statistic as extreme as, or more extreme than, the one calculated from the sample data, assuming the null hypothesis is true.
- If p-value < alpha (significance level), reject the null hypothesis.
- If p-value > alpha, fail to reject the null hypothesis.
- A small p-value suggests that the observed data is unlikely under the null hypothesis.
- Does not indicate the magnitude or importance of the effect.
Memory trick: If P is low, Null must go. If P is high, Null can fly.