{"id":33642,"date":"2021-04-25T11:22:20","date_gmt":"2021-04-25T15:22:20","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=33642"},"modified":"2021-04-29T13:29:19","modified_gmt":"2021-04-29T17:29:19","slug":"accurate-statistics","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/accurate-statistics\/","title":{"rendered":"How to Get Accurate Statistics: Good Sampling"},"content":{"rendered":"\n

How can you get accurate statics if you can’t possibly count every entity of a population? What criteria make a good sample?<\/p>\n\n\n\n

It is true that the only way to get a 100% accurate sample is to count every single entity, but that’s just impossible. That’s why statisticians use samples to represent a population. There are certain criteria that must be met to ensure that your sample is as accurate as possible.<\/p>\n\n\n\n

Keep reading to learn more about how good sampling<\/a> leads to accurate statistics.<\/p>\n\n\n\n\n\n\n\n

Accurate Statistics<\/strong><\/h2>\n\n\n\n

The only way to get perfectly accurate statistics is to count every entity that makes up the whole.<\/strong> For example, if you want to know how many red beans there are in a jar of red-and-white colored beans, the only way to find out for sure is to count all of the red beans in the jar.<\/p>\n\n\n\n

However, in most cases, counting every single entity is impossibly expensive and impractical. <\/strong>For instance, imagine you were trying to know how many red beans there are in every jar on the planet<\/em>\u2014you\u2019d have to count all the red beans in the world at any given time.<\/p>\n\n\n\n

To get around this problem, statisticians count a sample instead of the whole, assuming the sample\u2019s make-up proportionally represents the whole. <\/strong><\/p>\n\n\n\n

A sample must meet the following two criteria to actually <\/em>be representative of the whole (and thus, be \u201cgood\u201d):<\/p>\n\n\n\n

Criteria For Good Statistics<\/h3>\n\n\n\n

Criteria #1: Large. <\/strong>This reduces the effects of chance\u2014chance affects every survey, poll, and experiment, but when the sample size is large, its effects are negligible. <\/p>\n\n\n\n

  • For example, the probability of getting heads when flipping a coin is 50%. In practice, if you flip a coin 10 times, you\u2019re unlikely to get heads five times. You\u2019ll probably get some other number due to chance\u2014say, three. If you don\u2019t flip the coin any more times, you\u2019re left with the impression that the probability of getting heads is 3\/10, or 30%, which is clearly incorrect. You\u2019ll need to flip the coin 1,000 times to reduce the effects of chance and get a figure closer to the real probability of a half.<\/li><\/ul>\n\n\n\n

    How big your sample needs to be depends on what you\u2019re studying. For example, if the incidence of polio is one in 500, and you want to test a vaccine, you\u2019ll have to vaccinate far more than 500 people to get any meaningful results about the vaccine\u2019s efficacy. It’s hard to know if the vaccine works if, even without its use, only one person would have contracted the disease anyway.<\/p>\n\n\n\n

    Criteria #2: Random.<\/strong> Every entity in the complete group must have an equal chance of being selected to be part of the sample. Perfectly random sampling is too expensive and unwieldy to be practical. (Even if you were only going to randomly select one bean in 1,000 to be part of the sample, you\u2019d first need a list of every bean in the world to even determine where to find each thousandth bean.) Instead, statisticians use stratified random sampling, which works like this:<\/p>\n\n\n\n

    • Statisticians divide the whole into groups: for example, people over the age of forty, people under the age of forty, Black people, white people, and so on.<\/li>
    • They select samples from each group. How many are taken from each group depends on the group\u2019s proportion in relation to the whole.<\/li><\/ul>\n\n\n\n

      Avoiding Bias<\/h3>\n\n\n\n

      Despite statisticians\u2019 best efforts, bias is always present when choosing samples<\/strong> because:<\/p>\n\n\n\n

      • Statisticians might get the proportions wrong and over or under-represent certain groups.<\/strong><\/li>
      • Statisticians can\u2019t always tell which entity belongs to which group.<\/strong> (Shortform example: You might be able to tell how many people have red hair by looking at them, but how do you know if it\u2019s naturally <\/em>red?)<\/li>
      • When sampling people, interviewers may be biased in their choice of subjects.<\/strong> (For example, if an interviewer has the choice between two people from the same group, she might choose the one who looks friendlier, to make it easier to get her job done.)<\/li>
      • Interviewers might bias respondents.<\/strong> For example, one wartime poll asked Black people living in the South what they thought was more important, beating the Nazis, or bolstering democracy in the US. The Black interviewers found 39% of respondents prioritized beating the Nazis, while white interviewers found 62%. Black people might be more inclined to give white interviewers the answer they think they want, rather than tell them what they actually believe, so that they appear more loyal.<\/li><\/ul>\n","protected":false},"excerpt":{"rendered":"

        How can you get accurate statics if you can’t possibly count every entity of a population? What criteria make a good sample? It is true that the only way to get a 100% accurate sample is to count every single entity, but that’s just impossible. That’s why statisticians use samples to represent a population. There are certain criteria that must be met to ensure that your sample is as accurate as possible. Keep reading to learn more about how good sampling leads to accurate statistics.<\/p>\n","protected":false},"author":8,"featured_media":33658,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[15,25],"tags":[268],"class_list":["post-33642","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-education","category-statistics","tag-how-to-lie-with-statistics","","tg-column-two"],"yoast_head":"\nHow to Get Accurate Statistics: Good Sampling - Shortform Books<\/title>\n<meta name=\"description\" content=\"How can statisticians get accurate statistics despite not being able to count every entity of a population? 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