Why statistical tests validate hypotheses in R Programming - Quick Recap

Recall & Review

beginner

What is the main purpose of a statistical test in hypothesis validation?

A statistical test helps decide if the data supports or rejects a hypothesis by measuring how likely the observed results are if the hypothesis is true.

beginner

What does a p-value represent in hypothesis testing?

The p-value shows the probability of getting results at least as extreme as the observed ones, assuming the null hypothesis is true.

intermediate

Why do we set a significance level (alpha) before testing a hypothesis?

The significance level is a threshold to decide when to reject the null hypothesis, usually set at 0.05, meaning we accept a 5% chance of wrongly rejecting it.

intermediate

How does a statistical test use sample data to validate a hypothesis?

It compares the sample data to what we expect under the null hypothesis and calculates a test statistic to see if the data is unusual enough to reject the null.

beginner

What role does the null hypothesis play in statistical testing?

The null hypothesis represents the default assumption (no effect or no difference) that the test tries to challenge using data.

What does a low p-value indicate in hypothesis testing?

A The data is unlikely under the null hypothesis

B The null hypothesis is definitely true

C The sample size is too small

D The test was performed incorrectly

A low p-value means the observed data is unlikely if the null hypothesis is true, suggesting we may reject it.

What is the null hypothesis usually assumed to be?

A The data is biased

B There is a strong effect

C The alternative hypothesis

D There is no effect or difference

The null hypothesis usually states there is no effect or difference, serving as the starting assumption.

Why do we choose a significance level like 0.05?

A To limit the chance of wrongly rejecting the null hypothesis

B To guarantee the null hypothesis is true

C To increase the sample size

D To avoid calculating p-values

The significance level sets how much risk we accept for wrongly rejecting the null hypothesis.

What does a statistical test compare to validate a hypothesis?

A Two unrelated datasets

B Observed data vs expected data under null hypothesis

C The hypothesis vs the alternative hypothesis

D Sample size vs population size

The test compares observed data to what is expected if the null hypothesis is true.

What happens if the p-value is greater than the significance level?

A The test is invalid

B We reject the null hypothesis

C We fail to reject the null hypothesis

D The sample size must be increased

If p-value is greater than alpha, we do not have enough evidence to reject the null hypothesis.

Explain in your own words why statistical tests help validate hypotheses.

Think about how we decide if data supports or rejects an idea.

Describe the steps involved in performing a statistical test to validate a hypothesis.

Imagine you are explaining the process to a friend who has never done it before.