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The Journal of Hypertension has now produced statistical guidelines for authors. This review highlights some of the important issues in the guidelines.The Type I error is essentially the chance of a false positive result and is conventionally set to 5%. Equally important, but sometimes neglected, is the Type II error — the chance of a false negative result.Many statistical tests are only valid if the data are normally distributed. If the data are not normally distributed, non-parametric tests should be used.Type I error rates must be corrected if multiple comparisons are made.In general, regression coefficients are more useful than correlation coefficients. The latter may often be statistically significant but not convey any useful information.A confidence interval is a measure of the range which is likely to contain the true value of the parameter of interest; it conveys more information than a P value about the result of a study. Confidence intervals are also very useful for comparing studies.