The video tutorial explains the concept of margin of error in statistics, highlighting its importance when surveying a sample instead of an entire population. It discusses confidence levels, typically set at 95%, and how they affect the interpretation of survey results. Through examples, such as HR engagement surveys, the tutorial illustrates how to calculate the margin of error and the impact of sample size on its accuracy. The video concludes with recommendations on determining an appropriate sample size to achieve a desired margin of error.

What is the primary purpose of calculating the margin of error?

• To determine the exact value of a population parameter

• To measure the amount of error introduced by sampling

• To increase the sample size

• To decrease the confidence level

Why do researchers prefer using samples over entire populations?

• Samples are more accurate

• Entire populations provide biased results

• Sampling is less expensive and time-consuming

• Samples eliminate all errors

In the example calculation, what is the population size?

• 1500

• 9.5%

• 100

• 95

What confidence level is commonly used in business applications?

• 95%

• 90%

• 85%

• 99%

If a survey result shows 25% in favor with a margin of error of 9.5%, what is the range of the population's opinion?

• 25% to 35%

• 10% to 40%

• 20% to 30%

• 15.5% to 34.5%

In the HR survey example, what was the engagement score for 2020?

• 96%

• 77%

• 82%

• 91%

What does it mean when confidence intervals overlap?

• The margin of error is too large

• The results are significantly different

• The results are not significantly different

• The sample size is too small

How does increasing the sample size affect the margin of error?

• It decreases the margin of error

• It has no effect on the margin of error

• It increases the margin of error

• It makes the margin of error unpredictable

What is the margin of error if 400 responses are received from a population of 1500?

• 5%

• 10%

• 4.2%

• 9.5%