In randomised trials or repeated-measures experimental studies of randomised conditions, researchers often measure a continuous variable at baseline and at the end of the study at follow up. Examples of some outcomes include blood pressure, pain, physiological responses, range of motion, etc. In a BMJ statistics note, methodologists Andrew Vickers and Doug Altman explain how these outcomes can be analysed.
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Confidence intervals are widely reported in published research and are usually thought to provide more information than p values from significance tests because confidence intervals indicate how precise an estimate is. Sometimes, however, investigators report an estimate (eg. a mean) and p value, but not the confidence interval about the estimate. In a BMJ statistics note, statisticians Doug Altman and
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Every scientist has, at one point or another in their career, plotted results using bar graphs or dot plots with error bars. As pointed in a previous post, these kinds of summary graphs can be misleading, especially since a depressingly large number of scientists plot their error bars as the standard error of the mean (SEM) rather than the standard
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Many research questions investigate how outcomes change (over time) in response to different test conditions or treatments. Participants are randomised into groups to receive a test condition or treatment to make the groups comparable in every way except the test condition or treatment that is received. Consequently, comparing outcomes between groups allows us to understand how outcomes change under different
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In the previous post we learned that the standard error of a sample statistic indicates how precisely a population parameter is estimated, and the standard error depends on sample size. For a given standard deviation, a small standard error is obtained when sample size is large. The standard error indicates how variable the sample statistic is if the experiment is
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In the previous post we learned that a sample statistic (e.g., a sample mean) is used to estimate a population parameter (e.g., the population mean), and the standard error of the sample statistic indicates the amount of precision around the estimate of the population parameter. A small standard error indicates that a sample statistic estimates a population parameter with high
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