{"id":10905,"date":"2020-02-08T09:47:23","date_gmt":"2020-02-08T14:47:23","guid":{"rendered":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=10905"},"modified":"2020-02-08T09:47:23","modified_gmt":"2020-02-08T14:47:23","slug":"keeping-data-inclusivity-without-diluting-your-results","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/keeping-data-inclusivity-without-diluting-your-results\/10905\/","title":{"rendered":"Keeping Data Inclusivity Without Diluting your Results"},"content":{"rendered":"Originally published in WeAllCount.com, January 17, 2020 Let\u2019s say you are surveying 100 people out of 10,000. You want to analyze the data from your sample of 100 to get answers about the likely behaviors and preferences of the overall 10,000 person population. Part of your project focuses on equity among sexual orientations. You don\u2019t want to leave anyone out and you know that having a question about sexual orientation where people select \u2018heterosexual or homosexual\u2019 isn\u2019t inclusive enough. You consult experts and the local community and decide to include \u2018Heterosexual, Gay, Lesbian, Bisexual, Pan Sexual, or Asexual\u2019 <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/keeping-data-inclusivity-without-diluting-your-results\/10905\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Originally published in WeAllCount.com, January 17, 2020 Let\u2019s say you are surveying 100 people out of 10,000. You want to analyze the data from your sample of 100 to get answers about the likely behaviors and preferences of the overall 10,000 person population. Part of your project focuses on equity among sexual orientations. You don\u2019t [&hellip;]<\/p>\n","protected":false},"author":72,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11,48],"tags":[59,230,166,243,8],"class_list":["post-10905","post","type-post","status-publish","format-standard","hentry","category-industry-news","category-left-hand","tag-analytics","tag-data-analytics","tag-data-science","tag-machine-learning","tag-predictive-analytics"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/10905","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/72"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=10905"}],"version-history":[{"count":1,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/10905\/revisions"}],"predecessor-version":[{"id":10906,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/10905\/revisions\/10906"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=10905"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=10905"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=10905"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}