Student t-test

The t-test tests if there is a significant difference in Mean between two data sets (2 sample t-test) or one data set and a specific value (one sample t-test). The t-test also called "student's t-test" and follows the t distribution. This distribution is based on the normal distribution and whit a high sample size the shape is the same.

1 sample t-test

If Diff mean is selected and one of the comparing column contains 1 value the 1 sample t-test is calculated. To calculate the if there is a significant difference in Mean between a data-set and a Hypothesized value.

Interpretation

The smaller the p value is the more likely there is a significant difference between the data-set and the hypothesized value.
Develve uses the commonly accepted value of p < 0.05 for significance.
For a good power (0.8 in Develve) the sample size data sets must be bigger than the minimum sample size calculated.
For a good t-test the data-sets must be normally distributed see Anderson Darling normality test.
- If the data set is not normally distributed see "What to do with not normally distributed data". The 1 sample Wilcoxon median test is the non parametric counter part of the 1 sample t-test.

Options

Bigger/Smaller (one sided test) or not equal test (two sided)

Colors of the cells

Red
Data-set is not normally distributed
Green
No significant difference
Yellow
Significant difference
Orange
Sample size to small

Formula

With the t value and the degrees of freedom the program calculate p value.

Sample size

The result is the minimum sample size of the data-set.

not equal

bigger smaller

Example 1 sample t-test (not equal)

To use the 1 sample t-test test first unselect "non normal distributed" when the box is selected the 1 sample Wilcoxon median test is calculated. Then select Diff mean.

E is significant not equal with the data-set A and the sample size is big enough (Row t-test p <0.05).

Data file

2 sample t-test

If Diff mean is selected the 2 sample t-test is calculated between this column, and the comparing column, if this column and the comparing column contains more than 1 value. To calculate if there is a significant difference in Mean between the two data-sets.

Interpretation

The smaller the p value is the more likely there is a significant difference between the 2 data-sets.
Develve uses the commonly accepted value of p < 0.05 for significance.
For a good power (0.8 in Develve) the sample size for both data sets must be bigger than the minimum sample size calculated.
For a good t-test the data-sets must be normally distributed see Anderson Darling normality test.
- If the data set is not normally distributed see "What to do with not normally distributed data". The 2 sample Mann–Whitney-test is the non parametric counter part of the 1 sample t-test.

Options

Bigger/Smaller (one sided test) or not equal test (two sided)

Colors of the cells

Red
Data-set is not normally distributed
Green
No significant difference
Yellow
Significant difference
Orange
Sample size to small

Formula Calculating t value

Formula Calculating the amount of degrees of freedom

With the t value and the degrees of freedom the program can interpolate the p value out of the t table.

Sample size

is the biggest one of the two comparing data sets. The result is the minimum sample size for both data sets.

not equal

bigger smaller

Example 2 sample t-test (not equal)

To use the 2 sample t-test test first unselect "non normal distributed" when the box is selected the 2 sample Mann-Whitney median test is calculated. Then select Diff mean.

The difference between data set A and B is not significant and the sample size is to small (Row t-test p >0.05).
The difference between data set A and C is significant and the sample size is big enough (Row t-test p <0.05).
The difference between data set A and D is not significant and the sample size is big enough (Row t-test p >0.05).

Data file

Paired t test

The paired t test is a modified version of the 1 sample t test. To conduct a Paired t-test subtract the two comparing data stets and run a 1 sample t test on this resulting data set. By comparing this data set with a column with as only input 0.