SPSS Data Analysis--t test

The T-Test in SPSS is all concentrated in the analysis-compare mean menu. About the T-Test again, we know that a statistical result needs to be expressed in three parts: concentration, variability, and significance.

The centralized performance indicator is the mean value
Variance, standard deviation, or standard error is the performance indicator
The significance is to determine whether to achieve the significance level according to the statistic quantity

The T-Test based on T distribution is the test of the mean of the sample, which is a significant test of the mean difference because of the sampling distribution of the mean value of t distribution sample.

The T-test can be used in the following three types of analysis

1. Differential analysis of sample mean and population mean (single-sample T-Test)

2. Paired design sample mean or two non-independent two-digit mean difference analysis (paired T test)

3. Two independent sample mean difference analysis (Independent sample t test)

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I. Analysis-comparative mean-single-sample T-Test
The single sample t test is used to analyze the difference between the mean value of the sample and the overall mean value, in order to determine whether the sample from the population is equal to (greater than or less than) the mean value of a known population, the applicable condition is that the sample data distribution is normal distribution, the small sample case needs to be tested, the large sample case approximate normal Can be used as long as it is not a severe bias.




Ii. analysis-comparative mean-paired sample T-Test

When the paired design data is a continuous variable, you can use the paired T-Test, the paired T-test that if the two treatments do not actually differ, then the overall mean value of each pair of data should be 0, which is actually a single sample T test with a known mean of 0, so the applicable conditions are the same as the single sample T test.

SPSS Data Analysis--t test