Introduction

To generate hypotheses you often sample different groups or places (which is sometimes called a mensurative experiment because you usually measure something, such as height or weight, on each experimental unit) and explore these data for patterns or associations. To test hypotheses you may do mensurative experiments, or manipulative experiments where you change a condition and observe the effect of that change upon each experimental unit (like the experiment with millipedes and light described in Chapter 2). Often you may do several experiments of both types to test a particular hypothesis. The quality of your sampling and the design of your experiment can have an effect upon the outcome and determine whether your hypothesis is rejected or not. Therefore it is important to have an appropriate and properly designed experiment.

First, you should attempt to make your measurements as accurate and precise as possible so they are the best estimates of actual values.

1. Accuracy is the closeness of a measured value to the true value.

2. Precision is the ‘spread’ or variability of repeated measures of the same value.

For example, a thermometer that consistently gives a reading corresponding to a true temperature (e.g. 20℃) is both accurate and precise. Another that gives a reading consistently higher (e.g. + 10℃) than a true temperature is not accurate, but it is very precise.