When do you need statistical calculations?

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:

1. Important findings can be obscured by biological variability and experimental imprecision. This makes it difficult to distinguish real differences from random variation.
2. Conversely, 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.
When you work with experimental systems with no biological variability and little experimental error, heed these aphorisms:

1. If you need statistics to analyze your experiment, then you've done the wrong experiment.
2. If your data speak for themselves, don't interrupt!

In many fields, however, scientists can't avoid large amounts of variability, yet care about relatively small differences. Statistical methods are necessary to draw valid conclusions from such data.