![]() Then, if subjects are treated the same during the experiment (e.g. If an experiment is well planned, randomization makes the various treatment groups similar to each other at the beginning of the experiment except for the luck of the draw that determines who gets into which group. The issue of whether a result is unlikely to happen by chance is an important one in establishing cause-and-effect relationships from experimental data. 109.Ī statistically significant relationship is one that is large enough to be unlikely to have occurred in the sample if there's no relationship in the population. This lack of association is supported by a correlation of. In Example 5.3, the scatterplot does not show any strong association between exercise hours/week and study hours/week.Since the data points are very close to a straight line it is not surprising the correlation is -.903. In Example 5.2, the scatterplot shows a negative association between monthly rent and distance from campus.It is common for a correlation to decrease as sample size increases. However, there is still quite a bit of scatter around the pattern. In Example 5.1, the scatterplot shows a positive association between weight and height.The correlation is a descriptive result.Īs you compare the scatterplots of the data from the three examples with their actual correlations, you should notice that findings are consistent for each example. ![]() The correlation is calculated using every observation in the data set. ![]() This is because the correlation depends only on the relationship between the standard scores of each variable. The correlation is independent of the original units of the two variables. ![]()
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