5 articles to learn #statistics for #datascience:

1. Comprehensive Inferential Statistics for Data Science -https://bit.ly/2NUQywr
2. Master Hypothesis Testing for Framing Data Science Problems - https://bit.ly/2u0utmV
3. Introduction to #ANOVA (with practical #Excel examples) - https://bit.ly/2F1ciE5
4. Tutorial for Understanding Non-Parametric Statistical Tests - https://bit.ly/2CcSxrr
5. Learn Statistics using R! - https://bit.ly/2VMOOIr

✴️ @AI_Python_EN

💡 What is a p-value?

When testing an hypothesis, the p-value is the likelihood that we would observe results at least as extreme as our result due purely to random chance if the null hypothesis were true.

💡 What does it mean when a p-value is low?

When the p-value is low, it is relatively rare for the our results to be purely from random variations in observations.

Because of this, we may decide to reject the null hypothesis. If the p-value is below some pre-defined threshold, we say that the result is "statistically significant" and we reject the null hypothesis.

💡 What value is most often used to determine statistical significance?

A value of alpha = 0.05 is most often used as the threshold for statistical significance.

#datascience #statistics

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