\u00a0 \u00a0\u00a0 \u00a0\u00a0 tails:<\/strong> say you're not.<\/i><\/p>\nLong story short, if we have 100 responses we know that this will be made up of two distinct populations: the one we care about, a 'true population' of 50 responses (with a cheat\/no-cheat mix); and mixed in with it a second, false population of 50 made up of 25cheat\/25no-cheats. Simply strike that false population out, so for example if the survey says 30 cheats \/ 100 population, we end up with a more considered view: 5 cheats in 50.<\/p>\n
Simples. Our cheating friends can answer in full knowledge that the interviewer has no idea if the response was truthful or if generated by a tails\/head coin toss. Even better, the truth-teller knows that the interviewer will likely be forgiving and presume that the response was coin-toss generated.<\/p>\n
The best about this game? Everyone knows the rules and no cheating's required!<\/p>\n
Bayesian Inference<\/strong>
\nWe can actually take the above idea and develop much further with Bayesian analysis, but that is for another day.<\/p>\n<\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"A short blog about a cute algorithm we came across whilst reading on Bayesian Methods, a theme we may develop here as we build upon our machine learning skills. We want to know the level of cheating in the population. I think it’s safe to say that fewer cheats than reality – regardless of any […]<\/p>\n","protected":false},"author":2,"featured_media":2393,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"spay_email":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false},"categories":[3],"tags":[],"jetpack_featured_media_url":"https:\/\/circadian-capital.com\/wp-content\/uploads\/2015\/06\/privacy.png","jetpack_publicize_connections":[],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p9TEZs-Cq","_links":{"self":[{"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/posts\/2382"}],"collection":[{"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/comments?post=2382"}],"version-history":[{"count":10,"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/posts\/2382\/revisions"}],"predecessor-version":[{"id":2392,"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/posts\/2382\/revisions\/2392"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/media\/2393"}],"wp:attachment":[{"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/media?parent=2382"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/categories?post=2382"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/circadian-capital.com\/wp-json\/wp\/v2\/tags?post=2382"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}