DataLab is a compact statistics package aiming at exploratory data analysis. Please visit the DataLab Web site for more information....

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.