Statistics is easy case studies on real scientific datasets

Manpreet Singh Katari, Sudarshini Tyagi, Dennis Shasha

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy, gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.

Original languageEnglish (US)
Title of host publicationSynthesis Lectures on Mathematics and Statistics
Subtitle of host publicationLecture #37
PublisherMorgan and Claypool Publishers
Pages1-73
Number of pages73
Edition3
StatePublished - Apr 7 2021

Publication series

NameSynthesis Lectures on Mathematics and Statistics
Number3
Volume13
ISSN (Print)1938-1743
ISSN (Electronic)1938-1751

Keywords

  • Case studies
  • Data cleaning
  • Machine learning
  • Nonparametric statistics
  • Null value imputation
  • Scientific data

ASJC Scopus subject areas

  • Analysis
  • Statistics and Probability
  • Mathematics (miscellaneous)
  • Applied Mathematics

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