QM 2 - CHAP 4

One-sample t-test

A statistical test that compares the sample mean to a known or hypothesized population mean to determine if the sample mean is significantly different from the population mean.

Assumptions of a one-sample t-test

1. Scales of measurement: The dependent variable should be interval or ratio. 2. Normality: The data should be normally distributed. 3. Known population mean: The population mean must be known or meaningful. 4. Independence: Observations must be independent. 5. Random selection: The sample must be randomly selected.

Null hypothesis (H0) in a one-sample t-test

There is no difference between the sample's mean and the population mean.

Alternative hypothesis (H1) in a one-sample t-test

There is a significant difference between the sample's mean and the population mean.

Significant result (p < .05)

Indicates that the sample's average is significantly different from the population mean.

Non-significant result (p > .05)

Indicates that there is no statistically significant difference between the sample's mean and the population mean.

Paired-samples t-test

A statistical test that compares the means of two related measurements from the same group to determine if there is a significant difference.

Differences between paired-samples t-test and independent-samples t-test

Paired-samples t-test compares related measurements while independent-samples t-test compares means between two different, unrelated groups.

Within-subjects design

A research design where the same participants are measured multiple times.

Independent observations

In the context of statistical tests, each observation or response is independent of others.

Null hypothesis (H0) for a paired-samples t-test

There is no significant difference in the means between the two related measurements.

Alternative hypothesis (H1) for a paired-samples t-test

There is a significant difference in the means between the two related measurements.

Effectiveness of a therapy program

Determined through a significant difference in pre- and post-intervention scores.

SPSS output interpretation

Provides insights on test statistics and p-values to determine significance in research findings.