Understanding Significance Levels and Hypothesis Testing

The video discusses significance levels, denoted by alpha, and their importance in hypothesis testing. It uses Raheem's penny experiment to illustrate how different significance levels affect conclusions. The video explains p-values and emphasizes setting significance levels before experiments for ethical reasons.

What is the significance level denoted by in hypothesis testing?

• Greek letter delta

• Greek letter beta

• Greek letter alpha

• Greek letter gamma

In Raheem's experiment, what was the null hypothesis?

• Spinning results in tails more often

• Spinning increases the proportion of heads

• Spinning does not change the proportion of heads

• Spinning decreases the proportion of heads

How many times did Raheem spin each penny in his experiment?

• 5 times

• 10 times

• 20 times

• 15 times

What was the sample proportion of heads in Raheem's experiment?

• 0.59

• 0.50

• 0.49

• 0.60

What p-value did Raheem calculate for his experiment?

• 0.036

• 0.046

• 0.056

• 0.066

If Raheem set his significance level at 0.05, what conclusion would he make?

• Fail to reject the null hypothesis

• Accept the alternative hypothesis

• Accept the null hypothesis

• Reject the null hypothesis

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

• The alternative hypothesis is rejected

• The experiment is invalid

• The null hypothesis is rejected

• The null hypothesis is accepted

Why is it important to set significance levels before conducting an experiment?

• To increase the chances of rejecting the null hypothesis

• To make the experiment easier

• To avoid bias and maintain ethical standards

• To ensure the experiment is interesting

What is a potential ethical issue in hypothesis testing?

• Conducting multiple experiments

• Using a low significance level

• Using a large sample size

• Setting significance levels after seeing p-values

What should researchers avoid doing to maintain ethical standards?

• Using a control group

• Adjusting significance levels after seeing results

• Calculating p-values accurately

• Setting significance levels before the experiment