Fitness Science Explained by Michael Matthews

Author:Michael Matthews
Language: eng
Format: epub
Publisher: Oculus Publishers
Published: 2020-07-08T00:00:00+00:00

Statistical Inference

The primary purpose of statistics is to make some type of inference (logical conclusion based on evidence) about a population, based on a sample from that population.

For example, let’s say you have a classroom of people and want to know their average body weight. How might you go about it? The easiest way, of course, is to weigh everyone and calculate the average. What if you can’t do that, though? What if there are 200 people in your classroom? How might you calculate the average weight? This is more difficult, but it can be done through the use of statistics.

What you can do is choose 50 people from the room at random, calculate their average weight, and infer that the average weight of the entire room is probably similar. This won’t be perfect, of course, but it’ll be more right than wrong.

How can you increase the accuracy of that inference? Well, you can increase the sample size to, let’s say, 100 people, and recalculate. That’ll bring you closer to the true average, and as you continue to increase your sample size, you’ll get even closer.

The two key points here are:

Analyzing samples from a larger group will give you a reasonable estimate of whatever you’re measuring, but there will always be a certain amount of error.

The larger your sample size, the more accurate your estimates become (the amount of error decreases). The smaller your sample size, the less accurate your estimates become (the amount of error increases).

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