**Hypothesis**: A
testable explanation for observations. Science in general proceeds by an
incremental process of refuting null hypotheses. Evidence is then presented
persuasively in the form of a pattern that has been calibrated against
unmeasured variation.

The *null hypothesis*
is the refutable hypothesis of negligible systematic difference or pattern, in
other words that nothing interesting is going on beyond random variation.

The *test hypothesis*
is the alternative hypothesis of pattern, in the form of one or more real
treatment effects, as defined by the statistical model.

A statistical test will reject the null hypothesis with
probability *α* of doing so
mistakenly (and thereby making a 'Type-I error'),
or it will accept the null hypothesis with probability *β* of doing so mistakenly (and thereby making a 'Type-II error').
Data collection should be designed with a view to maximising the power to detect true effects at a given *α*, with power defined by the
probability 1 - *β*.

Doncaster, C. P. & Davey, A. J. H. (2007) *Analysis of Variance and Covariance: How to
Choose and Construct Models for the Life Sciences*. Cambridge: Cambridge
University Press.

http://www.southampton.ac.uk/~cpd/anovas/datasets/