Elementary Statistics for the Biological and Life Sciences

 STAT 205 is a three-credit, non-calculus course that introduces fundamental statistical methods and their applications in the biological and life sciences. It is intended for students in biology, ecology, public health, pharmacy, nursing, and related fields, with the goal of helping them recognize statistics as an essential tool for research and evidence-based decision-making.
Students develop practical skills in describing and interpreting data through graphs and summary statistics, applying basic probability concepts, and analyzing one- and two-sample problems. They learn the foundations of confidence intervals, hypothesis testing, sample-size and power calculations, and checking statistical assumptions. The course also covers simple linear regression, and 2×2 contingency tables. Students explore applied topics such as relative risk, odds ratios, sensitivity and specificity, predictive values, and disease rates.
By the end of the course, students should be able to interpret common statistical output, understand when key methods are appropriate, and conduct common statistical analyses using R.

Learning Outcomes/Objectives

•   Understand and interpret common graphical displays and summary statistics from data.
•  Apply the rules of probability to solve basic problems
•  Understand aspects of one and two sample problems, including confidence intervals, hypothesis testing, sample size calculation, power, and checking assumptions.
•   Understand basic ideas underlying one-way analysis of variance
• Understand aspects of the simple linear regression model: least squares estimation, the normal-errors model, confidence interval and hypothesis tests for slope
•  Understand the logistic regression model and its use for analyzing Bernoulli outcomes with a continuous predictor
• Understand aspects of 2x2 contingency tables: relative risk, odds ratio, difference in proportions, case-control studies, independence, sensitivity, specificity, and prevalence, predictive values positive and negative, Simpson’s paradox and the Cochran-Mantel-Haenszel test
•  Have a basic understanding of related ideas including receiver operator characteristic (ROC) curves, disease rates, incidence versus prevalence, and survival curves.
•  Be able to carry out common statistical methods in the computing package R.