John C. Pezzullo

To estimate sample size in biostatistics, you must state the effect size of importance, or the effect size worth knowing about. If the true effect size is less than the “important” size, you don’t care if the test comes out nonsignificant. With a few shortcuts, you can pick an important effect size and find out how many participants you need, based on that effect size, for several common statistical tests.

The so-called “one-way analysis of variance” (ANOVA) is used when comparing three or more groups of numbers. When comparing only two groups (A and B), you test the difference (A – B) between the two groups with a Student t test. So when comparing three groups (A, B, and C) it’s natural to think of testing each of the three possible two-group comparisons (A – B, A – C, and B – C) with a t test.

There are many approximate formulas for the CIs (confidence intervals) around an observed event count or rate (also called a Poisson CI). Suppose that there were 36 fatal highway accidents in your county in the last three months. If that's the only safety data you have to go on, then your best estimate of the monthly fatal accident rate is simply the observed count (N), divided by the length of time (T) during which the N counts were observed: 36/3, or 12.

Scientists conduct experiments on limited samples of subjects in order to draw conclusions that (they hope) are valid for a large population of people. Suppose you want to conduct an experiment to determine some quantity of interest. For example, you may have a scientific interest in one of these questions: What is the average fasting blood glucose concentration in adults with diabetes?

The basic idea of the median (that half of your numbers are less than the median) can be extended to other fractions besides 1/2. A centile is a value that a certain percentage of the values are less than. For example, 1/4 of the values are less than the 25th centile (and 3/4 of the values are greater). The median is just the 50th centile.

You can run the Student t tests using typical statistical software and interpret the output produced. In this example, you'll be using the software package OpenStat. The basic idea of a t test All the Student t tests for comparing sets of numbers are trying to answer the same question, "Is the observed difference larger than what you would expect from random fluctuations alone?

As you dive deeper into the field of biostatistics, you'll need to develop a firm understanding of pharmacokinetics (PK) and pharmacodynamics (PD) and the differences between the two. The term pharmacokinetics (PK) refers to the study of How fast and how completely the drug is absorbed into the body (from the stomach and intestines if it's an oral drug) How the drug becomes distributed through the various body tissues and fluids, called body compartments (blood, muscle, fatty tissue, cerebrospinal fluid, and so on) To what extent (if any) the drug is metabolized (chemically modified) by enzymes produced in the liver and other organs How rapidly the drug is eliminated from the body (usually via urine, feces, and other routes) The term pharmacodynamics (PD) refers to the study of The relationship between the concentration of the drug in the body and the biological and physiological effects of the drug on the body or on other organisms (bacteria, parasites, and so forth) on or in the body.

Histograms are bar charts that show what fraction of the subjects have values falling within specified intervals. The main purpose of a histogram is to show you how the values of a numerical value are distributed. This distribution is an approximation of the true population frequency distribution for that variable.