Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.

H. G. Wells

When analyzing data, your goal is simple: You wish to make the strongest possible conclusion from limited amounts of data. To do this, you need to overcome two problems:

•Important findings can be obscured by biological variability and experimental imprecision. This makes it difficult to distinguish real differences from random variation.

•The human brain excels at finding patterns, even in random data. Our natural inclination (especially with our own data) is to conclude that differences are real and to minimize the contribution of random variability. Statistical rigor prevents you from making this mistake.

Statistical analyses are necessary when observed differences are small compared to experimental imprecision and biological variability.

Some scientists ask fundamental questions using clean experimental systems with no biological variability and little experimental error. If this describes your work, you can heed these aphorisms:

•If you need statistics to analyze your experiment, then you've done the wrong experiment.

•If your results speak for themselves, don't interrupt!

Other scientists work in fields where they look for relatively small differences in the face of large amounts of variability. In these fields, statistical methods are essential.