Our goal is to record statistics that can be used to estimate standard deviation or standard error. Many different methods can be used to summarize data, and this is reflected in the diversity of statistics that are reported. An overview of these methods is given in tab:statname and a description below.
Where available, direct estimates of variance are preferred, including Standard Error (SE), sample Standard Deviation (SD), or Mean Squared Error (MSE).
SE is usually presented in the format of
mean SE
.
MSE is usually presented in a table.
When extracting SE or SD from a figure, measure from the mean to the upper or lower bound.
This is different than confindence intervals and range statistics (described below), for which the entire range is collected.
If MSE, SD, or SE are not provided, it is possible that LSD, MSD, HSD, or CI will be provided. These are range statistics and the most frequently found range statistics include a Confidence Interval (95%CI), Fisher's Least Significant Difference (LSD), Tukey's Honestly Significant Difference (HSD), and Minimum Significant Difference (MSD). Fundamentally, these methods calculate a range that indicates whether two means are diffent or not, and this range uses different approaches to penalize multiple comparisons. The important point is that these are ranges and that we record the entire range.
Another type of statistic is a ``test statistic''; most frequently there will be an F-value that can be useful, but this should not be recorded if MSE is available. Only if there is no other information available, record the P-value.
statname | name | definition | notes |
SD | Standard Deviation, ![]() |
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SE | Standard Error |
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|
MSE | Mean Squared Error | like SD, but with multiple treatments. in R:
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|
95%CI | 95% Confidence Interval |
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measure the 95% CI from the mean, this is actually ![]() |
LSD | Least Significant Difference |
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MSD | Minimum Significant Difference |
David 2011-10-07