Statistics for Data Science and Business Analysis - Test for the Mean; Population Variance Unknown

The video tutorial covers hypothesis testing, focusing on a scenario where a marketing analyst estimates a competitor's email open rate. It explains the formulation of null and alternative hypotheses, the use of T-statistic for small samples, and decision-making based on T-score and P-value analysis. The tutorial concludes with a recommendation to practice exercises.

What is the null hypothesis in the scenario of comparing email open rates?

• The mean open rate is exactly 40%.

• The mean open rate is not related to 40%.

• The mean open rate is higher than 40%.

• The mean open rate is lower or equal to 40%.

Why is the T statistic used instead of the Z statistic in this scenario?

• Because the population variance is unknown and the sample size is small.

• Because the sample size is large.

• Because the data is not normally distributed.

• Because the population variance is known.

What is the calculated T score in this hypothesis test?

• 0.53

• 1.83

• -0.53

• -1.83

What conclusion can be drawn if the absolute value of the T score is lower than the critical value?

• Reject the null hypothesis.

• Accept the alternative hypothesis.

• Cannot reject the null hypothesis.

• The test is inconclusive.

If the P value is greater than the significance level, what decision should be made regarding the null hypothesis?

• Reject the null hypothesis.

• Accept the alternative hypothesis.

• Cannot reject the null hypothesis.

• The test is invalid.