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The importance of statistics in biological data analysis (course announcement)
Submitted by J.W. Bizzaro; posted on Thursday, March 15, 2007

In today's world of high-throughput experiments, we deal with laboratory equipment constantly churning out mountains of data. But without an understanding of statistics and a knowledge of the techniques required to analyze, summarize and interpret these data, we are very limited in what we can learn from our observations, which will in turn inhibit our ability to move forward in our research. Even with experiments that generate very little data, there is a need to simulate phenomena by modeling the behavior of systems and their parameters, which again often needs to be done statistically. It is therefore imperative that we understand the basics of probability, statistical distributions, descriptive statistics, and some simple parametric hypothesis tests.

Bioinformatics.Org is offering a new course next week that reviews fundamental statistical methods from the perspective of a biologist. The various statistical distributions covered will help you know when assumptions can be made about a normal distribution and how to test whether or not these assumptions are true. Essential descriptive statistics such as mean, variance, median, minimum, maximum and quantile are reviewed and then used in various situations to calculate background, noise, normalization and thresholding. Additionally, hypothesis testing by t-tests is introduced so that you can assess groups of observations for a particular parameter and calculate whether or not the difference between groups is significant. Data visualization using various graphs will also be reviewed.

Armed with these techniques, you will be able to better deal with the challenges of data analysis. Plus, you'll be able to understand and interpret data at a more fundamental level and draw the correct conclusions about them. And the entire course will be conducted in R, a free and open-source package for statistical computing that has become an essential part of the biostatistician's toolbox.