Significance [sɪɡˈnɪf.ə.kəns]: A measure of certainty of a statistical result that is used in hypothesis testing. In other words, the result is unlikely to have occured if the hypothesis you want to disprove were true. To be statistically significant, the result would have to be less likely than a significance level chosen beforehand. That level might be 5 % or 1 %, for example. If that’s the case, you win: You’d reject the hypothesis you wanted to disprove since it’s so unlikely to be true.

Significance Level

Significance Level [sɪɡˈnɪf.ə.kəns ˈlev.əl]: A threshold for the probability of a wrong decision when testing a hypothesis. Usually called alpha (α). Common choices are α = 0.05 (5 %) or α = 0.01 (1 %). If the calculated p-value is lower than the significance level, the result is called statistically significant.

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Simpson’s paradox

Simpson’s paradox [ˈsɪmps(ə)nz ˈpærədɒks]: A phenomenon in statistics by which one can derive opposite conclusions from the same data, depending on if you look at the data as a whole or separate them by a specific factor. A correlation observed in all parts of the data does not have to be a correlation that can be found in the dataset as a whole and vice versa. It was first described in 1951 by Edward H. Simpson.