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Understanding statistical significance is critical from "summary" of Predictive Analytics by Eric Siegel
Statistical significance is a crucial concept in predictive analytics. It is essential to grasp this concept in order to interpret the results of statistical analyses accurately. When a result is deemed statistically significant, it means that the observed effect is unlikely to have occurred by random chance alone. In other words, the relationship or pattern identified is likely to be real and not just a fluke. Understanding statistical significance is critical because it helps us determine whether a particular variable truly has an impact on the outcome we are trying to predict. Without considering statistical significance, we may mistakenly believe that a relationship exists when, in fact, it is just the result of random variation in the data. This can lead to faulty predictions and misguided decisions. Statistical significance is typically assessed by calculating a p-value, which represents the probability of observing the results (or more extreme results) if there is no true effect. A p-value below a ...
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