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The first resource we created was ‘Data Fallacies to Avoid’, an illustrated collection of mistakes people often make when analyzing data.
1. Cherry Picking
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.
2. Data Dredging
Data dredging is the failure to acknowledge that the correlation was, in fact, the result of chance.
3. Survivorship Bias
Drawing conclusions from an incomplete set of data, because that data has ‘survived’ some selection criteria.
4. Cobra Effect
When an incentive produces the opposite result intended. Also known as a Perverse Incentive.
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5. False Causality
To falsely assume when two events occur together that one must have caused the other.
6. Gerrymandering
The practice of deliberately manipulating boundaries of political districts in order to sway the result of an election.
7. Sampling Bias
Drawing conclusions from a set of data that isn’t representative of the population you’re trying to understand.
8. Gambler’s Fallacy
The mistaken belief that because something has happened more frequently than usual, it’s now less likely to happen in future and vice versa.
9. Hawthorne Effect
When the act of monitoring someone can affect that person’s behaviour. Also known as the Observer Effect.
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