Statistics: The basics

Information on research, statistics and publications - tips including how to recruit participants, gain funding, understand your results and get them published.
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Paul Wicks
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Statistics: The basics

Post by Paul Wicks » Fri Mar 30, 2007 8:57 am

For a basic overview of statistical principles, go to:
http://www.statisticshell.com/html/apf.html

Levels of data I remember by the french word for black; NOIR

Nominal - Categories like male / female

Ordinal - Numerical things that can be ranked but not measured e.g. Likert 1-5 scale (disagree -> agree strongly)

Interval - Numerical things with an even gap in between them but with an arbitrary zero e.g. time, height above sea level, longitude

Ratio - Numerical things with an even gap with an absolute zero, e.g. weight, height, temperature. Also sometimes called continuous data.

Parametric tests like the T-Test have a set of assumptions that go with them. For instance the data you use must normally be at the very least ordinal level data, preferably interval. These tests assume that the data you're looking at is at least similar to the normal distribution (that good old bell curve you know and love).

If the data is not normally distributed (number of legs in the population is a nicely skewed one!) or the level of data is only categorical than you're looking at a non-parametric test. You might also need to use one of these if the data is highly skewed or if your standard deviation is very wide. As a rule of thumb if your standard deviation is almost as big as your mean then you've probably not got a normally distributed set of data. You should also use a non-parametric test when you have a small sample size; this will vary depending on what you're measuring but you'd be hard-pressed to justify a parametric test with a sample size of less than 15-20 per group.

That said some tests like ANOVA are quite robust despite data not conforming to the normal distribution. Then it all starts becoming a little fuzzier...

When reporting on post-hoc test differences include the magnitude of the difference e.g. "A post-hoc Bonferronni test revealed that sporadic ALS patients performed more poorly than control participants (mean difference = 2.6 points, 95% CI = 0.61 - 4.65, p=0.017)."

There are lots of short primers on stats on the net. So well worth a Google if you get beyond the Andy Field stuff.

Last checked by qualified clinical psychologist (BlueCat) on 23/05/2016

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