Biology 4121 - Biometry

Biology 4121 - Biometry

Statement of Goals

To recognize the different kinds of biological data (especially measurement variables versus attributes).
To generally know the appropriate kinds of statistical tests to apply to different biological data.
To appreciate the role and usefulness of statistics in presenting and interpreting biological data.
To understand the basic principles of hypothesis testing in statistics and their similarities to the scientific method.
To understand and appreciate that all statistical thinking is based on probability.
To fully appreciate the meaning of P < 0.05.
To understand and fully appreciate the difference between statistics and parameters.
To understand and appreciate the difference between parametric and nonparametric statistics and when to use them.
To generally recognize the main categories of statistical tests (analysis of variance, regression, contingency tables, etc.) and be able to state what these are used for.
To understand and test for the assumptions common to most statistical procedures.
To be able to present summaries of biological data appropriately either in table or in figure form.
To be able to critique the statistics used in a biological paper.
To thoroughly understand the general linear model used in so many statistical procedures, and explain the differences between various models.
To be able to understand the difference between Type I and Type II errors in statistics and how these relate to power.
To thoroughly understand basic terminology such as level of significance, critical value, null and alternate hypothesis, etc.
To foster the hypothesis testing approach to looking at biological data so that questions can be framed in terms of null hypotheses.
To become familiar with SAS, a major statistical package widely used throughout the world in solving statistical problems but to also recognize any given statistical package will be different from others and will require relearning.
To generally recognize the different analysis of variance designs (crossed, nested, repeated measures, etc.).
To recognize paired or blocked data and know the statistical procedures for dealing with such data.
To understand and distinguish between the various types of regression versus correlation and the implications of using any of these.
To appreciate the value of first looking at the distribution of any biological data to detect outliers, mistakes, non-normality, and the like.