Common Statistical Errors

Check out this journal article  (pdf) on common statistical errors in medical experiments. Don't worry this is only the medical research that's meant to keep you alive and the statistical tests that prove its effectiveness are often not up to scratch. Anyway below are the errors that they identified. 

Statistical errors and deficiencies related to the design of a study

• Study aims and primary outcome measures not clearly stated or unclear

• Failure to report number of participants or observations (sample size)

• Failure to report withdrawals from the study

• No a priori sample size calculation/effect-size estimation (power calculation)

• No clear a priori statement or description of the Null-Hypothesis under investigation

• Failure to use and report randomisation

• Method of randomisation not clearly stated

• Failure to use and report blinding if possible

• Failure to report initial equality of baseline characteristics and comparability of study groups

• Use of an inappropriate control group

• Inappropriate testing for equality of baseline characteristics

Statistical errors and deficiencies related to data analysis

• Use of wrong statistical tests

• Incompatibility of statistical test with type of data examined

• Unpaired tests for paired data or vice versa     

• Inappropriate use of parametric methods

• Use of an inappropriate test for the hypothesis under investigation

• Inflation of Type I error

• Failure to include a multiple-comparison correction

• Inappropriate post-hoc Subgroup analysis

• Typical errors with Student’s t-test

• Failure to prove test assumptions

• Unequal sample sizes for paired t-test

• Improper multiple pair-wise comparisons of more than two groups

• Use of an unpaired t-test for paired data or vice versa

• Typical errors with chi-square-tests

• No Yates-continuity correction reported if small numbers

• Use of chi-square when expected numbers in a cell are <5

• No explicit statement of the tested Null-Hypotheses

• Failure to use multivariate techniques to adjust for confounding factors 

Errors related to the documentation of statistical methods applied

• Failure to specify/define all tests used clear and correctly

• Failure to state number of tails

• Failure to state if test was paired or unpaired

• Wrong names for statistical tests

• Referring to unusual or obscure methods without explanation or reference

• Failure to specify which test was applied on a given set of data if more than one test was done

• “Where appropriate” statement

Statistical errors and deficiencies related to the presentation of study data

• Inadequate graphical or numerical description of basic data

• Mean but no indication of variability of the data

• Giving SE instead of SD to describe data

• Use of mean (SD) to describe non-normal data

• Failure to define ± notion for describing variability or use of unlabeled error bars

• Inappropriate and poor reporting of results

• Results given only as p-values, no confidence intervals given

• Confidence intervals given for each group rather than for contrasts

• “p = NS”, “p <0.05” or other arbitrary thresholds instead of reporting exact p-values

• Numerical information given to an unrealistic level of precision

Statistical errors and deficiencies related to the interpretation of study findings

• Wrong interpretation of results

• “non significant” interpreted as “no effect”, or “no difference”

• Drawing conclusions not supported by the study data

• Significance claimed without data analysis or statistical test mentioned

• Poor interpretation of results

• Disregard for Type II error when reporting non-significant results

• Missing discussion of the problem of multiple significance testing if done

• Failure to discuss sources of potential bias and confounding factors