Sampling in Statistics [Coming Soon]

Use samples to draw inferences about a population.

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In this lesson, students use random sampling to draw inferences about a population, and draw informal comparative inferences about two populations.

Understand that statistics can be used to gain information about a population by examining a sample of the population.
Use random sampling tends to produce representative samples and support valid inferences.
Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability.
Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations.

1. Overlapping distribution makes comparing two groups difficult
2. Determining whether the difference in the mean of the two distributions is meaningful
3. Larger differences in mean can compensate for high variability
4. Larger sample size leads to a more meaningful difference
5. Median and interquartile range (IQR) to compare two groups
1. Descriptive vs Inferential Statistics
2. Sample and population
3. Can we draw conclusions about a population by examining any sample?
4. Using representative sample to answer questions about the population
5. A biased sample tends to overrepresent or underrepresent certain values
6. Generating a (simple) random sample
1. Predicting population mean using a sample mean
2. How confident are we in our predictions?
3. The sampling distribution
4. Does the sample size influence our estimation of the population mean?
5. Is a random sample better at predicting the population mean when it is drawn from a population with less variability?
6. More on sampling distribution