![]() Significance is expressed as a probability that your results have occurred by chance, commonly known as a p-value. The concept of significance simply brings sample size and population variation together, and makes a numerical assessment of the chances that you have made a sampling error: that is, that your sample does not represent your population. Where there is more variation, there is more chance that you will pick a sample that is not typical. ![]() ![]() You can assess this by looking at measures of the spread of your data (and for more about this, see our page on Simple Statistical Analysis). However, another element also affects the accuracy: variation within the population itself. Your sample size strongly affects the accuracy of your results (and there is more about this in our page on Sampling and Sample Design). One of the best ways to ensure that you cover more of the population is to use a larger sample. This would have serious implications for whether your sample was representative of the whole population. However, you might also be unlucky (or have designed your sampling procedure badly), and sample only from within the small red circle. If it is all from within the yellow circle, you would have covered quite a lot of the population. However, it is more likely to be smaller. When you take a sample, your sample might be from across the whole population. In the diagram, the blue circle represents the whole population. Understanding Statistical Distributions.Area, Surface Area and Volume Reference Sheet.Simple Transformations of 2-Dimensional Shapes.Polar, Cylindrical and Spherical Coordinates. ![]()
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