Statistical significance testing
Statistical analysis of numeric data
Undertake statistical analysis of numeric data, grouped by a category variable.
Statistical tests include t-test, one-way analysis of variance (ANOVA), Wilcoxon rank sum test
and Kruskal-Wallis test. Choice of test depends on user input (parametric or non-parametric)
and the number of groups identified in the data.
- the type of analysis to be done (parametric or non-parametric);
- whether the data is continuous or discrete;
- desired level of confidence for confidence interval estimation;
- the desired number of digits after the decimal point in summary results; and
- two columns of data (including a header row with labels for each column). The first column is the category variable and the second is the numeric variable.
Note missing values in the data will be omitted from calculations.
The program undertakes statistical tests depending on the options selected and also outputs a tabular summary and graphs of the data.
- a summary of statistical test results for: t-test (2 groups) or ANOVA (>2 groups) if the parametric option is selected or Wilcoxon rank sum test (2 groups) or Kruskal-Wallis test (>2 groups) for the non-parametric option;
- Shapiro-Wilk test for normality on all groups and Bartlett's test for homogeneity of variance if the parametric option is selected;
- Note: for groups with < 4 or > 5000 observations it is not possible to calculate Shapiro-Wilks W statistic to test for normality of distribution;
- a numeric summary of the data for each group and overall, including histograms and normal plots;
- Note: for groups with only one observation or with standard deviation = 0 histograms and quantile plots are not shown;
- a table and bar chart of frequency counts for each cell if the data is identified as being discrete; and
- a bar chart of the sample size per group, boxplots of the data by group and a plot of confidence intervals about the mean value for each group.