T-test

A student T-test is sufficient to figure out the difference in means.

In the case of one and two sample location problems, a t-test does this.

A t-test was used to examine for significance in differences.

As with the serial t-test, such a data set does not best show the potential of this technique.

Statistical analysis Students's t-test was used to evaluate the difference between two values.

The physical performance measures were analyzed by the student's paired t-test.

The t-test was created to care about this difference.

• T-tests are used to determine differences in average scores between two groups.

A T-test was used to identify significant pressure change.

The effect of the treatment can be analyzed using a one-sided t-test.

Changes in mean scale scores over time were analysed using paired t-tests.

C Data can be evaluated using a students t-test.

The paired t-test will generally have greater power to detect differences than the sign test.

Explicit expressions that can be used to carry out various t-tests are given below.

A one-tailed t-test was performed and the following results were obtained.

Following, is an example of a data set that can be analyzed using the t-test for dependent samples.

Several t-tests were carried out between conditions in order to find which hypothesis was most strongly supported.

This distribution is important in studies of the power of Student's t-test.

Either the sign test or a two-sample t-test could be used to determine if there is a significant difference between boys and girls.

For a comparison between two groups, the t-test is the most commonly used procedure in biological research.

The numbers of cells in blastocysts were analyzed using Student t-test.

Statistical analysis was performed using the t-test for paired observations.

Paired t-tests were used to compare regional means within a given protocol.

Tukey's test is based on a formula very similar to that of the t-test.

Student's t-tests are appropriate for comparing means under relaxed conditions when less is assumed.