Theory and Practice of Nutritional Science

Abstract

Introduction to intellectual and practical ABCs of biomedical science, including (1) measurement & quantification; 2) experimental design; (3) descriptive & analytic statistics; (4) computerized data analysis, graphing, & literature searches; (5) data interpretation, hypothesis testing; (6) writing and publishing scientific papers, preparing oral & poster presentations.

Objective

Each scientific specialty has its own particular theoretical and factual content and its own vocabulary. These vary so much that scientists even in closely related fields often have difficulty communicating with each other. Despite this, almost all science is based on very similar underlying concepts and practices. The goal of this class is to introduce this basic “toolbox” to beginning nutritional scientists. The class is organized into several modules of varying length, each of which will include both didactic presentations and practice exercises to be completed by the students. The modules include: (1) quantification: operationalism; measurement theory; measurement scales, continuous and discrete variables and their distributions; mathematical probability; (2) experimental design: types of control groups and their interpretations in clinical and basic research; exploration or discovery science vs. verification or hypothesis testing; construction and testing of scientific hypotheses; (3) statistics: choice and execution of descriptive and analytic statistics of sample data; data transformations; choice of parametric and nonparametric tests; the basics of some tests (binomial; chi2, binomial, ranks tests, t-tests, ANOVA); sampling errors; statistical significance and power; a priori and post-hoc tests, especially after ANOVA; (4) computerization: introduction to appropriate computer programs for statistical analysis, for graphical displays of data, and for searching the scientific literature; (5) scientific logic: Interpretation of data in relation to hypotheses, control groups, and statistical test outcomes; uses of positive vs. negative data; role of replication; the concept of causality in science; inductive and deductive logic; 6) expressing quantitative outcomes in words; comparisons of data to previous publications; composition of written summaries and critiques of information in scientific publications; identifying strengths and weaknesses of existing data; appropriate citation of previous authors, including rules for using their thoughts and words, (7) writing and publishing scientific papers; peer review and publication process; preparation of oral and poster presentations.

Resources