Applied approach to statistical investigation, focusing on real-
world decision making in the face of uncertainty. Introduction to
central limit theorem and sampling distributions from
probabilistic and simulations frameworks for inference, including
one- and two-sample inference for means, proportions, simple
regression, and categorical data. Consequences of Type I, Type II
errors.
Athena Title
Intro to Stat for Life Science
Equivalent Courses
Not open to students with credit in STAT 3110E
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Student Learning Outcomes
Students will use statistical software to compute appropriate summary statistics and graphs and identify important characteristics to provide practical insights about biological processes.
Students will assess the validity of a study regarding causation and identify sources of bias and critique an experimental study in terms of being able to fulfil its goal.
Students will conduct statistical inference (confidence intervals and hypothesis tests) for a variety of biological data, interpret the results as they relate to the population of interest, and recognize the shortcomings of inferential results.
Students will recommend an appropriate statistical model for quantitative and categorical data relevant to the life sciences, conduct this analysis on statistical software, and explain the broader implications of the results.
Students will describe in writing the results of statistical analysis using non-technical language suitable for non-statisticians.
Topical Outline
The course will start with descriptive statistics and the principles of sampling. The course will also cover confidence intervals and hypothesis testing involving means and proportions from one and two populations; calculation of type II errors, power, and paired comparisons; simple linear regression; introduction to categorical data.
