Table 2. Summary of Common Errors in Statistics.

<Statistical errors in the design of a study>

Error in choosing the research type that can best prove the conclusion

Unclear descriptions of study objectives, hypotheses, and variables measured to test the hypotheses

Absence of hypotheses description

Error in sample size calculation

Absence of a description of the effect size

Inaccurate description or missing description of a randomized trial

Insufficient description of a blind study

Missing information about the homogeneity between compared groups with respect to basic characteristics

<Statistical errors in data analysis>

Application of analytical methods which are inappropriate for the type of data

Unnecessary dichotomization of continuous-type data

Error in the application of parametric/non-parametric test methods

Basic statistical assumptions unchecked

Generation of type I error: multiple comparison error, with corrections not implemented

Exact test or continuity correction not implemented with categorical data having a small sample size

Misinterpretation of correlation as a causal relationship

<Errors in the documentation, presentation and interpretation of statistical analysis>

Absence of a detailed description of each statistical method applied to each data set

Omission of two-tailed/one-tailed test information

Reason for applying an unusual statistical method and a detailed explanation of the method not given

Incorrect names of statistical methods

Confusing the standard deviation with the standard error of mean or not mentioning which is which

Providing results with only the significance level without mentioning the confidence interval

Significance level presented as 'P = NS' or 'P < 0.05'

Misinterpretation of 'insignificance' as 'ineffective' or 'no difference'

Not considering the possibility of type II errors when reporting insignificant results

Making conclusions not derived from the results

Not reporting missing data

Nonconformity to the CONSORT reporting requirements