The Reporting of Statistical Significance in Scientific Journals

By Hoem, Jan | Demographic Research, January-June 2008 | Go to article overview

The Reporting of Statistical Significance in Scientific Journals


Hoem, Jan, Demographic Research


Reflexion

Scientific journals in most empirical disciplines have regulations about how authors should report the precision of their estimates of model parameters and other model elements. Some journals that overlap fully or partly with the field of demography demand as a strict prerequisite for publication that a p-value, a confidence interval, or a standard deviation accompany any parameter estimate. I feel that this rule is sometimes applied in an overly mechanical manner. Standard deviations and p-values produced routinely by general-purpose software are too often taken at face value and included without questioning, and features that have too high a p-value or too large a standard deviation are too easily disregarded as being without interest because they appear not to be statistically significant. In my opinion authors should be discouraged from adhering to this practice, and flexibility rather than rigidity should be encouraged in the reporting of statistical significance. One should also encourage thoughtful rather than mechanical use of p-values, standard deviations, confidence intervals, and the like. Here is why:

1. The scientific importance of an empirical finding depends much more on its contribution to the development or falsification of a substantive theory than on the values of indicators of statistical significance. It is important that authors be guided by a process of discovery and not blinded by a lack of statistical significance in the description of an empirical pattern. This means that authors should feel free to report findings that appear not to be statistically significant, provided that this fact is also reported. Indicators of statistical significance should not be suppressed, but authors should avoid using them mechanically. Authors should also not lose from sight the possibility that a lack of difference between two parameters (reflected in a lack of statistical significance of an effect) may be a scientifically interesting finding in itself.

2. Measures of statistical significance may be misleading. When a model has been developed through repeated use of tests of significance to include and exclude co-variates, to split or combine levels on categorical covariates, and to determine other model features, the user often loses control over statistical-significance values, and the values computed by standard software may be completely misleading. If one mechanically includes the p-values cranked out by standard software, this serves sooner to mislead than to inform.

3. Standard p-values can be insufficiently precise indicators of statistical significance, particularly if their values are given only in grouped levels, which are often indicated by asterisks beside parameter estimates ("* = p < 0:1, ** = p < 0:05, *** = p < 0:01", and so on). Then standard deviations and confidence intervals are much more precise when used appropriately. Incidentally, I would discourage the practice of printing standard deviations just underneath estimated parameters in tabular representations, since this can make it hard to see patterns in parameter estimates. If they are included at all, indicators of statistical significance should be presented in a manner that facilitates the interpretation of results, perhaps by listing them in separate table columns when appropriate. Significance asterisks are a poor substitute for this.

4. Standard deviations, when used, should be reported for interesting contrasts, not for features selected automatically by statistical software. In many demographic applications, parameters are contrasts between regression coefficients for the various levels of a categorical regressor, often presented as relative risks in comparisons between the "effect" of one regressor level and a baseline level on the same regres-sor. Standard software routinely selects the first (or last) level on such a regressor as its baseline level, and parameters measure deviations in the "effect" of having a different level from the baseline on the regressor. …

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