Statistical Tests

Command:

Math -> Tests...

DataLab offers various statistical tests to be performed. The general procedure for performing any statistical test is as follows:

1) mark the data to be tested. Depending on the test to be performed it may be required to mark two blocks of data

2) select the test to be performed by clicking the corresponding button

3) reply to the questions asked by DataLab (if any)

4) the results are displayed in the protocol window

Please note, that the statistical tests as implemented in DataLab are performed on an "as is" basis. This means that the assumptions about the data are not checked (for example, the prerequisite that the variances are to be equal is not checked when performing a two-sample t-test). You have to check the validity of the underlying assumption at your own.

DataLab currently offers the following statistical tests:

Comparing Means and Medians
One-Sample t-Test	Comparison of the mean of a sample with a predefined limit
Two-Sample t-Test	Comparison of the means of two samples with equal variances
Welch Test	Comparison of the means of two samples with unequal variances
Mann-Whitney U-Test	Comparison of the means of two samples of unknown distribution
t-Test for Paired Observations	Comparison of the means of paired samples(1)
One-way ANOVA	Simultaneous comparison of several means
Wilcoxon Signed Rank Test for Matched Pairs	Comparison of the medians of two pairwise related samples
Two-Sample Median Test	Comparison of the medians of two samples. If the number of marked values is less than 1000, the probabilities obtained both from the Chi2 distribution and the exact Fisher test are calculated.
Comparing Variances
Chi-Square Test	Comparison of the variance of a sample with a predifined limit
F Test	Comparison of the variances of two samples
Siegel-Tukey Test	Non-parametric comparison of the variances of two samples
Bartlett's Test	Simultaneous comparison of several variances
Levene's Test	Simultaneous comparison of several variances
Checking Distributions(1)
Kolmogorov-Smirnov Test	Testing against a standard normal distribution (mean = 0.0, std.dev. = 1.0)
Pearson's Chi2 Test	Tests whether two distributions are equal. This test comes in two variants: (1) assuming the marked values being observations and (2) the marked values being frequencies.
Lilliefors Test	Testing for normality (any mean or standard deviation)
Shapiro-Wilk Test	Testing for normality of small samples
Skewness Test	Testing for skewed distributions
Kurtosis Test	Testing for mesokurtic distributions
Checking Correlations
Significance of the Correlation Coefficient	Comparison of the correlation coefficient with a predefined limit
Tests for Outliers
Variance/IQR-Test	Three simples checks for outliers
Dean-Dixon Test	Testing the minimum and the maximum of the data
Grubbs Test	Test for outliers (assuming a normal distribution)

(1)	The t-test for matched pairs requires the differences of the pairs to be normally distributed. In order to support the testing for normality of the paired differences the normality tests automatically calculate the results for the differences of two samples if they are pairwise related.