A Consumer's Gide To Statistics: Correlation Is Not Causation

I started off this series of articles a long while ago with how to spot a biased sample and how to interpret results of a study with an obviously biased sample - basically, don't take it at face value and all that.

Thinking about it, that should not have been the first article in the series. This should have been, as it covers one of the very first and most important rules that will be covered in any basic statistics course - correlation does not imply causation.

A Consumer's Guide to Statistics: Sampling Bias

Over the next while - probably a long while, since I have yet to even think of all the topics I'm going to cover - I'm going to write a series of articles aimed at helping normal people understand statistics and, more importantly, when they're not to be trusted.

According to my studies, statisticians are not normal people, so this will not be aimed at them. Results are inconclusive as to whether or not they are people at all, however, so I will still try to not offend their sensibilities too much.

What's A Sample?

As you might have guessed from a title, this article is all about samples. So, what's a sample?

Formally, the sample is the subset of the population from which you have gathered your data for a given statistical experiment. Sometimes the sample is the population, but this is usually not the case, mostly for silly reasons like 'expenses' and 'logistics.'