Statistics Explained An Introductory Guide for Life Scientists

Introduction

Scientists do mensurative or manipulative experiments to test hypotheses. The result of an experiment will differ from the expected outcome under the null hypothesis because of two things: (a) chance and (b) any effect of the experimental condition or treatment. This concept was illustrated with the chi-square test for nominal scale data in Chapter 6.

Although life scientists work with nominal scale variables, most of the data they collect are measured on a ratio, interval or ordinal scale and are often summarised by calculating a statistic such as the mean (which is also called the average: see Section 3.5). For example, you might have the mean blood pressure of a sample of five astronauts who had spent the previous six months in space and need to know if it differs significantly from the mean blood pressure of the population on Earth. An agricultural scientist might need to know if the mean weight of tomatoes differs significantly between two or more fertiliser treatments. If you knew the range of values within which 95% of the means of samples taken from a particular population were likely to occur, then a sample mean within this range would be considered non-significant and one outside this range would be considered significant. This chapter explains how a common property of many variables measured on a ratio, interval or ordinal scale data can be used for significance testing.