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A Statistical Odyssey

Learn about common statistical misconceptions with an interactive space adventure. Recommended for ages 12 and up.

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★★★★★
What a fun way to learn statistics!
— Denali

This course is about common statistical fallacies that can lead to errors in interpreting information.

As you explore a spaceship to discover the cause of a mysterious ship-wide memory loss, you will learn how biases can distort our understanding of data. Recognizing and overcoming these errors helps us critically evaluate the information we encounter and better understand the world around us.

Topics covered are selection bias, regression to the mean, correlation vs. causation, and the clustering illusion.

What is selection bias?

Selection bias occurs when the participants chosen for a study or survey do not represent the whole group. For example, suppose you want to know what percentage of the population likes chocolate, and you conduct your survey at a chocolate factory. In that case, your survey results will be skewed by selection bias.

What is regression to the mean?

If a variable is unusually high or low on one occasion, it will likely be closer to the average on the next occasion. This phenomenon is called regression to the mean. For example, if an athlete performs exceptionally well one day, chances are they will have a closer to average performance the next day.

Imagine a coach praises athletes whenever they do well and notices that they tend to perform worse the next day. If he assumes that the poorer performance is caused by the praise, he ignores the more likely explanation that it is the result of regression to the mean.

What are correlation and causation?

Correlation means two variables move together. Causation indicates one variable directly affects the other. A correlation between two things can exist with or without causation. For instance, ice cream sales and drowning incidents are correlated because both increase in summer. However, eating ice cream doesn't cause drowning. Assuming that correlation always means indicates can lead to false conclusions.

What is the clustering illusion?

If you look at a collection of randomly generated points on a map, you will likely see some clusters of points. This is because randomly spaced points are not necessarily evenly spaced.

Similarly, if you flip a coin 100 times, you will likely see a streak of heads somewhere in there—this is just what randomness looks like.

When we see meaning or patterns in clusters or streaks that are simply random, we fall for the clustering illusion.