Sample Size and Data Variability

The video tutorial focuses on Grade 7 statistics, specifically on sample variability. It explores how the ratio of sample size to population affects data variability. The lesson emphasizes the importance of random, representative, and proportional samples. Using a neighborhood vehicle count example, the teacher demonstrates how small sample sizes lead to greater variability in mean calculations. By increasing the sample size, the variability decreases, resulting in more reliable data analysis. The tutorial concludes that larger sample sizes provide more accurate predictions about the population.

What is the central question of the lesson on sample variability?

• How does the sample size affect the mean?

• What is the effect of sample size on data variability?

• How to calculate the mean of a sample?

• Why are random samples important?

Why is it important for samples to be proportional?

• To increase the sample size

• To ensure samples are random

• To reduce the number of samples needed

• To make statistical analysis more reliable

In the vehicle count example, what was the initial sample size used?

• 10 homes

• 50 homes

• 15 homes

• 4 homes

What was the mean number of vehicles in the first small sample?

• 5

• 8

• 6

• 4

What was the range of means observed in the small sample analysis?

• 5 to 7

• 4 to 8

• 6 to 10

• 3 to 9

How did increasing the sample size affect the variability of the data?

• It increased the variability

• It had no effect

• It decreased the variability

• It made the data less reliable

What was the mean number of vehicles in the larger sample?

• 4.6

• 4.2

• 5.0

• 6.0

What conclusion can be drawn about sample size and data reliability?

• Sample size has no impact on reliability

• Larger samples provide more reliable data

• Smaller samples are more reliable

• Larger samples increase variability

What was the observed mean range in the larger sample analysis?

• 3 to 9

• 4 to 5

• 5 to 7

• 4 to 6

Why is it important to repeat the sampling process multiple times?

• To reduce the number of samples needed

• To observe variability and reliability

• To ensure randomness

• To increase the sample size