Overview

The previous lecture was on Lecture 8

That lecture presented the statistical procedures that permit researchers to use a sample mean to test hypotheses about a population. These statistical procedures were based on a few basic notions, which are summarized as follows:

1. A sample mean is expected to more or less approximate its population mean. This permits us to use the sample mean to test a hypothesis about the population mean.

2. The standard error provides a measure of how well a sample mean approximates the population mean. The standard error formula is:
   

3. To quantify our inferences about the population, we compare the obtained sample mean with the hypothesized population mean by computing a z-score test statistic. The Z-Test statistic's formula is:
   

There is one major problem with this: We don't usually know the population's standard deviation, which is required to compute the z-score's standard error.

This lecture

This lecture presents the statistical procedures that permit researchers to use a sample mean to test hypotheses about a population, when the population standard deviation is NOT known.

These procedures use the T-Test, rather than the Z-Test. They are based on T-Scores, which we first met in the lecture notes for Topic 5 .

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