Overview of what VIT modules cover


The above follows the order of: "motivation"; "implementation"; "investigating how well it works" for first randomised experiments and then random sampling. The range of measures and techniques covered is circumscribed by their lending themselves to a cohesive suite of visualisations built around features you can see on primary plots of data. [Breaking: While VIT is desktop software packaged with iNZight, VITonline, an online version of VIT that plays in your browser, is under development. The sampling variation and bootstrap confidence interval modules are working for basic situations.]

Randomisation Variation

This module caters for the effects of (artificial) random allocation to groups on: This module facilitates visual exploration of the nature and extent of variation in features of a numeric variable or a binary variable from user-supplied data, due solely to an artificial random labelling of individuals as belonging to different "groups".

Randomisation Tests (randomisation-based significance tests)

This module caters for randomisation tests on user-supplied data for: (Note: Two variables must be supplied.)

Sampling variation (sampling distributions)

This module caters for the variation incurred by sampling from user-supplied population data on: This module facilitates visual exploration of the nature and extent of variation in features of binary or numeric variables when samples are repeatedly taken from user-supplied population data.

An appreciation of the variation produced by random sampling is a central foundation for motivating statistical inferences based upon sampling theory in general and upon bootstrap resampling in particular.

Bootstrap Confidence intervals

This module caters for bootstrap confidence interval construction from user-supplied data for:

Confidence Interval Coverage

This module facilitates visual investigation of coverage frequencies of confidence intervals calculated from samples taken from user-supplied population data using both the boostrap and normal-theory.

This module caters for:

Bootstrap Significance Tests

Currently at the design stage.