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Clojure for Data Science

You're reading from   Clojure for Data Science Statistics, big data, and machine learning for Clojure programmers

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Length 608 pages
Edition 1st Edition
Languages
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Author (1):
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Henry Garner Henry Garner
Author Profile Icon Henry Garner
Henry Garner
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Table of Contents (12) Chapters Close

Preface 1. Statistics FREE CHAPTER 2. Inference 3. Correlation 4. Classification 5. Big Data 6. Clustering 7. Recommender Systems 8. Network Analysis 9. Time Series 10. Visualization Index

The Bonferroni correction


We therefore require an alternative approach while conducting multiple tests that will account for an increased probability of discovering a significant effect through repeated trials. The Bonferroni correction is a very simple adjustment that ensures we are unlikely to make Type I errors. It does this by adjusting the alpha for our tests.

The adjustment is a simple one—the Bonferroni correction simply divides our desired alpha by the number of tests we are performing. For example, if we had k site designs to test and an experimental alpha of 0.05, the Bonferroni correction is expressed as:

This is a safe way to mitigate the increased probability of making a Type I error in multiple testing. The following example is identical to ex-2-22, except the alpha value has been divided by the number of groups:

(defn ex-2-23 []
  (let [data (->> (load-data "multiple-sites.tsv")
                  (:rows)
                  (group-by :site)
                  (map-vals (partial...
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