Statistical fallacies are common tricks data can play on you, which lead to mistakes in data analysis. Learn what these fallacies are (along with real-life examples) and how you can avoid them when analyzing data.
The practice of selecting results that fit your claim and excluding those that don’t. The worst and most harmful example of being dishonest with data.
Data dredging is the failure to acknowledge that the correlation was in fact the result of chance.
Drawing conclusions from an incomplete set of data, because that data has ‘survived’ some selection criteria.
When an incentive produces the opposite result intended. Also known as a Perverse Incentive.
To falsely assume when two events occur together that one must have caused the other.
The practice of deliberately manipulating boundaries of political districts in order to sway the result of an election.
Drawing conclusions from a set of data that isn’t representative of the population you’re trying to understand.
The mistaken belief that because something has happened more frequently than usual, it’s now less likely to happen in future and vice versa.
When the act of monitoring someone can affect that person’s behavior. Also known as the Observer Effect.
When something happens that’s unusually good or bad, over time it will revert back towards the average.
A phenomenon in which a trend appears in different groups of data but disappears or reverses when the groups are combined.
Relying solely on metrics in complex situations can cause you to lose sight of the bigger picture.
A more complex explanation will often describe your data better than a simple one. However, a simpler explanation is usually more representative of the underlying relationship.
How interesting a research finding is affects how likely it is to be published, distorting our impression of reality.
It can be misleading to only look at the summary metrics of data sets.
Here's a handy poster of the first 15 data fallacy lessons.
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