Statistics Explained An Introductory Guide for Life







CAMBRIDGE UNIVERSITY PRESS | ISBN-13 978-0-521-83550-3 | English | PDF | Size: 2.53 MB | 281 Pages



Introduction

Why do life scientists need to know about experimental design and statistics?

If you work on living things it is usually impossible to get data from every individual of the group or species in question. Imagine trying to measure the length of every anchovy in the Pacific Ocean, the haemoglobin count of every adult in the USA, the diameter of every pine tree in a plantation of 200 000, or the individual protein content of 10 000 prawns in a large aquaculture pond. The total number of individuals of a particular species present in a defined area is often called the population. Since a researcher usually cannot measure every individual in the population (unless they are studying the few remaining members of an endangered species), they have to work with a carefully selected subset containing several individuals, often called experimental units, that they hope is a representative sample from which the characteristics of the population can be inferred. You can also think of a population as the total number of artificial experimental units possible (e.g. the 125 567 plots of 1m2 that would

cover a coral reef) and your sample being the subset (e.g. 20 plots) you have chosen to work with. The best way to get a representative sample is usually to choose a proportion of the population at random – without bias, with every possible experimental unit having an equal chance of being selected. The trouble with this approach is that there are often great differences among experimental units from the same population. Think of the people you have seen today – unless you met some identical twins (or triplets etc.), no two would have been the same. Even species that seem to be made up of similar looking individuals (like flies or cockroaches or snails) show great variability. This leads to several problems.




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