{"id":636,"date":"2013-06-30T17:00:02","date_gmt":"2013-06-30T16:00:02","guid":{"rendered":"https:\/\/blogs.bmj.com\/adc\/?p=636"},"modified":"2013-06-24T22:24:59","modified_gmt":"2013-06-24T21:24:59","slug":"statsminiblog-significance-tests-step-two","status":"publish","type":"post","link":"https:\/\/blogs.bmj.com\/adc\/2013\/06\/30\/statsminiblog-significance-tests-step-two\/","title":{"rendered":"StatsMiniBlog: Significance tests. Step two."},"content":{"rendered":"

\"\"Now last time we examined the core question<\/a> of ‘what is a statistical test asking’ with the answer\u00a0\u00a0‘what’s the likelihood that the results I’ve got from these two groups are different only because of the play of chance?’<\/p>\n

Now depending on what sort of data you have<\/a> will depend on what ‘the results from these two groups’ looks like. It might be that the data are continuous and Normal<\/a> (so we need parametric <\/a>tests), or continuous and non-Normal, or categorical (so we need non-parametric tests).
\n
\nThe choice of test, usually, falls out like this for continuous variables:<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Repeated tests in same individuals \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Tests in different individuals \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Testing >2 groups<\/p>\n

Parametric \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 paired t-test \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0(unpaired) t-test \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0ANOVA<\/p>\n

Non-Parametric \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Wilcoxon rank-sum test \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Mann-Whitney U test \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Kruskal-Wallis<\/p>\n

With these tests you are essentially asking ‘how likely is it that the middleish value<\/a> of the groups is different only because of the play of chance?’<\/p>\n

For categorical variables you’ll be wanting the chi-squared <\/strong>test.\u00a0(Note;\u00a0\u03c72 is not just for squares – 2×2 tables – but can be 3×2, 4×6 or whatever.) What you’re asking here is “how likely is it that the proportion of items in each column, if we look at each row in turn,\u00a0different only because of the play of chance?’<\/p>\n

And, basically, that’s that.<\/p>\n

– Archi<\/p>\n","protected":false},"excerpt":{"rendered":"

Now last time we examined the core question of ‘what is a statistical test asking’ with the answer\u00a0\u00a0‘what’s the likelihood that the results I’ve got from these two groups are different only because of the play of chance?’ Now depending on what sort of data you have will depend on what ‘the results from these […]<\/p>\n

Read More…<\/a><\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[79,2676],"tags":[],"class_list":["post-636","post","type-post","status-publish","format-standard","hentry","category-archimedes","category-stats"],"yoast_head":"\nStatsMiniBlog: Significance tests. Step two. - ADC Online Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/blogs.bmj.com\/adc\/2013\/06\/30\/statsminiblog-significance-tests-step-two\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"StatsMiniBlog: Significance tests. 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