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2: Introduction

Last updated

Sep 4, 2024
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- 1.2: Chapter 1 References and Suggested Readings
- 2.1: Why (Bio)Statistics?

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Chapter 1: Getting Started presented a brief introduction to statistical thinking, a systematic approach to how we describe and ask questions about the world from data, and a justification for why undergraduate biology students should learn biostatistics. In my day, most of us took statistics as part of our graduate training. The curriculum for science students has accelerated now — it is now assumed that as part of undergraduate career students gain experience working with data and developing quantitative reasoning skills. Biostatistics courses are designed to help you achieve this understanding.

First up, let’s sell Why biostatistics?

2.1: Why (Bio)Statistics?
Overview of the basic elements of biostatistics. Why studying statistics is a vital part of education and a career for biologists.
2.2: Why do we use R software?
Reasons for using R for statistics, rather than spreadsheet programs or commercial statistical software. Benefits of accessing R on a personal computer rather than in the cloud.
2.3: A brief history of (bio)statistics
Some definitions of terms used in biostatistics. A brief overview of the history of biostatistics, including its historical applications in epidemiology and eugenics.
2.4: Experimental Design and rise of statistics in medical research
Basic definitions of terms used in experimental design. Examples of situations where statistics can be applied to answer medical questions.
2.5: Scientific method and where statistics fits
How statistics fits in the scientific method, including discussion of Frequentist vs. Bayesian approaches, planned vs. unplanned comparisons, and top-down vs. bottom-up approaches for designing experiments.
2.6: Statistical reasoning
How critical thinking should be applied to statistics. Identifying bias.
2.7: Chapter 2 References and Suggested Readings

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