Understanding Research - Statistical Significance

I decided that, during my vacation, you’ll get a series of short posts, labeled “Understanding Research”.

Each post will be about a specific keyword or concept that might not be universally known or often misunderstood.

Let’s do this! The word for today is Statistical Significance.

Scientists have found out that …
We’ve all read that sentence more than once. It can usually be found in some news article describing the latest discoveries of some researchers. Sometimes they even link the paper in their sources, sometimes they don’t. But did you ever click the paper? Did you ever check if the findings have any statistical significance?

I doubt it if you’re not involved in research yourself. But to have an informed opinion about a subject it’s important to be able to understand the research.

Let’s have an example:

There are three people, Mary, John, and Peter. An alien observes them for a year and does experiments, not knowing anything about humans. When the year ends, the alien writes a paper which contains the following statement.

During the 12 months I observed the test subjects I noticed that one of the subjects, Mary gets violently sick every time she consumes any product made from a cow’s milk while Peter and John can drink it without any issues. I thus conclude that female humans are not able to safely drink cow’s milk while male humans are.

See, that doesn’t make sense. The alien took three random humans and by chance, one of them was lactose intolerant and female. A number of three test subjects is clearly not sufficient. But what is?

When collecting results, you always have to assume that a number of those results is totally random. The goal is to collect so many results that those random outliners are visible as just that – random outliners.

So the bigger the reference group, the bigger the chance it isn’t all just a coincidence.

If you’re reading a paper where they only tested about 30 people, it’s often less trustworthy than a paper where they tested 100,000 people. Sure, the second paper can have other faults but it’s an easy way to get a first idea about how trustworthy the results are.

Don’t just believe what some journalist tries to break down for you. Read the source if available.

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Read More:

Understanding Statistical Significance

Statistical Significance – Wikipedia

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