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Numbers in the News: A Survey

This report presents the prevalence of statistical terms and ideas in news stories. This involves (1) a judgment analysis of 160 articles and (2) a computer-match analysis of 899 articles. The judgment analysis of 160 articles involved 73 traits. These included (a) elements of study design, (b) statistical measures, and (c) types of grammar used to describe and compare ratios. Of these articles, 27% involve assembly in constructing categories or measures and 62% present associations that imply causation. Using judgment we find the following influences plausible: 42% influenced by confounding, 17% by assembly, 11% by bias and 9% by random effects. About 79% of the articles are based on samples. Of those articles, 89% gave the sample size but only 3% stated that the sample was random and none gave a p-value. Many studies described populations, for example all patients treated for a condition at predetermined hospitals during a fixed time period. Such articles never discussed why these results might or might not generalize to a larger population. The computer-match of 899 number-based news articles calculated the prevalence of 231 statistical terms. In this set, 19% use the word “significant,” but only 3% use “statistically significant” and none use “statistical significance”. Of these articles, over 90% used comparisons, 74% used percent grammar, 67% used causal grammar, 61% used association grammar, 55% used chance grammar, 32% used rate grammar, 13% used percentage grammar and 10% used confounding grammar. This empirical data should be useful in determining topics and their emphasis in teaching statistical literacy as applied critical thinking.