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

The video tutorial covers hypothesis testing, focusing on tests drawn from single and multiple populations. It explains testing with known variance using a data scientist salary example, detailing the calculation of the Z score and the use of critical values. The process of hypothesis testing is discussed, including decision rules and significance levels. The tutorial concludes with a discussion on varying significance levels and hints at future topics.

What are the two types of tests introduced in the video?

• Tests for qualitative and quantitative data

• Tests for large and small sample sizes

• Tests for known and unknown variances

• Tests drawn from a single population and multiple populations

In the data scientist salary example, what is the null hypothesis?

• The mean salary is different from $113,000

• The mean salary is $113,000

• The mean salary is $15,000

• The mean salary is $100,200

What is the purpose of standardizing a variable in hypothesis testing?

• To reduce the variance

• To compare it to a standard normal distribution

• To increase the sample size

• To make the variable easier to calculate

What is the Z score in the data scientist salary example?

• -4.67

• 1.96

• 2.58

• 0.05

What is the critical value for a 5% significance level in a two-sided test?

• 1.96

• 2.58

• 0.05

• 4.67

At a 1% significance level, what is the critical value used in the example?

• 1.96

• 4.67

• 2.58

• 0.01

What happens if the Z score is higher than the critical value?

• Accept the null hypothesis

• Reject the null hypothesis

• Increase the sample size

• Decrease the significance level