Mean ± S.E. assumes normality unless N is large; best to use a confidence interval instead of using one S.E.
Median is always representative of a continuous variable, although it is not as precise an estimate of central tendency if the distribution is truly normal
Use percentiles instead of "outside 3 s.d." for lab parameters
Some software makes a test of normality and depending on the result runs a parametric or nonparametric test. The problems with this approach include:
The test of normality may not have adequate power to detect non-normality
Even if the data come from a truly normal distribution, nonparametric tests are almost as efficient as parametric tests (Wilcoxon-Mann-Whitney test is 0.96 as efficient as t -test).
If the data are non-normal, the nonparametric test can be much more powerful than the parametric counterpart.
(r = product-moment linear correlation coefficient)
Geometric means are typically not good for describing central tendency of skewed data. Geometric means are greatly affected by low outliers and may be difficult to interpret.
Interpretation of P -Values
P -values only provide evidence against a hypothesis, never evidence in favor of it.
P =.8 implies there is lack of evidence of an effect, i.e., either:
There is little or no effect or
There is insufficient information in the sample due to small N or high variability --- "Absence of evidence is not evidence for absence" (Altman and Bland, BMJ 311:485; 1995)
P =.01 implies there is evidence for an effect, but this effect may be clinically insignificant
P =.05 in many cases provides very little evidence against the null hypothesis
Confidence intervals convey much more information than P -values, especially when P is large