Basic Statistics for Life Scientists: A Concise Handbook of Essential Techniques 1st Edition By Jakub Tomek, David Eisner
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Basic Statistics for Life Scientists: A Concise Handbook of Essential Techniques 1st Edition By Jakub Tomek, David Eisner

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191
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2.28 MB
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Digital PDF
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eBook[PDF]
About this ebook
Basic Statistics for Life Scientists: A Concise Handbook of Essential Techniques (1st Edition) by Jakub Tomek and David Eisner focuses on practical, jargon-free statistical applications designed specifically for biomedical and life science researchers. [1, 2]
The book is organized into structured chapters that guide researchers through data handling, test selection, experimental design, and avoiding common analytical pitfalls: [1]
1. Data Summarization and Visualization
  • Numerical summaries: Basic techniques for descriptive statistics.
  • Data visualization: Best practices for plotting and visualizing life science data. [1]
2. The p-Value and Statistical Significance
  • Core concepts: Understanding what a p-value actually represents.
  • Misconceptions: Overcoming common misinterpretations of binary statistical significance. [1, 2]
3. Core Statistical Tests
  • Parametric & Non-parametric: Coverage of \(t\)-tests (paired, unpaired, Welch), ANOVA (one-way, two-way, repeated measures), and alternatives like Mann–Whitney/Wilcoxon tests.
  • Association & Modeling: Frequency tables (Fisher's exact, Chi-square), correlation (Pearson, Spearman, Kendall), and regression models.
  • Resampling: Introduction to permutation tests. [1]
4. Common Methodological Pitfalls
  • Design & Analysis: Addressing multiple testing, underpowered studies, and proper power calculations.
  • Data Integrity: Strategies to avoid pseudoreplication, \(n\)-hacking, and improper data handling. [1]
5. Experimental Design
  • Best Practices: Guidelines on pre-registration, data structuring, randomization, and blinding to ensure robust results. [1]
Supplementary Materials
  • Practical Application: Includes a software overview and code samples for analysis, visualization, and testing. [1]

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Topics

ANOVA (one-way two-way repeated measures)c Association & Modeling: Frequency tables (Fisher's exact Chi-square) correlation (Pearson Spearman Kendall) regression models. Resampling: Introduction to permutation tests Data Integrity: Strategies to avoid pseudoreplication Data Summarization and Visualization Data visualization: Best practices for plotting and visualizing Design & Analysis: Addressing multiple testing Mann–Whitney/Wilcoxon tests misinterpretations of binary statistical significance Numerical summaries: Basic techniques for descriptive statistics Parametric & Non-parametric: Coverage of \(t\)-tests Tests (paired unpaired Welch) The p-Value and Statistical Significance Understanding what a p-value actually represents. \(n\)-hacking and improper data handling
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