Role of Statistics in Biology
While the study of biology focuses on living organisms, statistical analyses provide crucial insight into many biological processes. Basic statistical concepts help biologists correctly prepare experiments, verify conclusions and properly interpret results. Many biology courses of study require a course in biostatistics that covers such concepts as randomized trials, hypothesis testing and the use of statistical software.
Establishing Sample Size
An important part of any biological experiment involves correctly choosing samples and selecting the right number of trials. A basic introduction to statistics provides background in statistical randomness and the law of large numbers. When conducting a study of whether insects prefer to eat American elm leaves or Princeton elm leaves, for example, using a properly randomized sample of both types of leaves helps control for confounding factors. For example, choosing a small number of leaves from the American elm, if those were all from a single tree, might result in a tree that was unusually filled with insects and would bias the results. Selecting many hundreds of leaves from a random sample of trees, however, reduces this type of error.
When conducting experiments with a large sample, a biologist must make sure that a conclusion is statistically significant. One such experiment might involve examining whether smoking leads to cancer. By examining the means of two groups -- one that smokes and one that doesn't -- a biologist might find that the smokers more commonly had cancer. Means, however, reflect the center of a distribution of data, and hypothesis testing involves examining the spread of that distribution. If the data is heavily clustered around the mean, the mean number is a reliable indicator; if the data is heavily spread out, the mean is less reflective of overall trends and should be considered in that context.
Interpreting Data Analyses
After finishing an experiment or observation, biologists need statistics to draw proper conclusions. For example, comparing the data from two groups of plants -- one that was watered and one that was not -- can lead to erroneous conclusions. A biologist, for example, might simply record the mean height of these two sets of plants and conclude that the watered plants grew taller. This, however, does not account for other statistical measures, such as variance. The nonwatered plants might have grown less tall on average, but perhaps their heights varied more widely than their watered counterparts, which is important data to relay in a conclusion.
Very large data sets cannot easily be processed by hand. In many biological situations, such as those in ecology that use large sample sizes, using statistical software makes data processing more expedient. Data programs include Stata; Statistical Analysis System, or SAS; and Statistical Product and Service Solution, or SPSS. Most introductory statistics classes will use these software products, which can involve learning programming languages.
Kevin Wandrei has written extensively on higher education. His work has been published with Kaplan, Textbooks.com, and Shmoop, Inc., among others. He is currently pursuing a Master of Public Administration at Cornell University.