2 years ago

May 15, 2015

Z-Scores

This week's episode dicusses z-scores, also known as standard score. This score describes the distance (in standard deviations) that an observation is away from the mean of the population. A closely related top is the 68-95-99.7 rule which tells us that (approximately) 68% of a normally distributed population lies within one standard deviation of the mean, 95 within 2, and 99.7 within 3.

Kyle and Linh Da discuss z-scores in the context of human height. If you'd like to calculate your own z-score for height, you can do so below. They further discuss how a z-score can also describe the likelihood that some statistical result is due to chance. Thus, if the significance of a finding can be said to be $3 \sigma$, that means that it's 99.7% likely not due to chance, or only 0.3% likely to be due to chance.

plot of chunk unnamed-chunk-1

Calculate the z-score of your height

Male Female

Assumed standard deviation: inches

Ft: Inches:

z-score:
percentile:

Using the interface above, select your gender and enter your height, and your z-score will be calculated. Additionally, this page reports the percentile - the percentage of the population which is shorter than you.



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\n\nMale\nFemale\n\n

\n\nAssumed standard deviation:\n inches\n\n

\n\n\n\t\n\t\t\n\t\t\n\t\n
Ft:\n\t\t\t\n\t\tInches:\n\t\t\t\n\t\t
\n\n
\n\n\n\t\n\t\t\n\t\t\n\t\n\t\n\t\t\n\t\t\n\t\n
z-score:
percentile:
\n\n
\n\n

Using the interface above, select your gender and enter your height, and your z-score will be calculated.\nAdditionally, this page reports the percentile - the percentage of the population which is shorter than you.

\n\n

\n\n\n \n\n","date_discovered":"2015-05-15","contributor":{"prettyname":"Kyle Polich","img":"https://s3.amazonaws.com/dataskeptic.com/contributors/kyle-polich.png","twitter":"@dataskeptic","linkedin":"https://www.linkedin.com/in/kyle-polich-5047193","bio":"Kyle studied computer science and focused on artificial intelligence in grad school. His general interests range from obvious areas like statistics, machine learning, data viz, and optimization to data provenance, data governance, econometrics, and metrology.","sort-rank":1},"last_rendered":"2016-11-19","publish_date":"2015-05-15"},"pagination":{"current":0,"next":1,"prev":0},"blog_focus":{"blog":{"prettyname":"/episodes/2015/z-scores","ext":".Rhtml","guid":"6f45383e523b8da393b058122b8b247c","c_hash":"0aa43bac26eb63140612739c465bff71","related":[],"author":"Kyle","uri":"dataskeptic.com/episodes/2015/z-scores.Rhtml","env":"master","desc":"This week's episode dicusses z-scores, also known as standard score. This score describes the distance (in standard deviations)\nthat an observation is away from the mean of the population. A closely related top is the\n68-95-99.7 rule which tells us that (approximately) 68% of a normally\ndistributed population lies within one standard deviation of the mean, 95 within 2, and 99.7 withi","rendered":"episodes/2015/z-scores.htm","title":"Z-Scores","date_discovered":"2015-05-15","contributor":{"prettyname":"Kyle Polich","img":"https://s3.amazonaws.com/dataskeptic.com/contributors/kyle-polich.png","twitter":"@dataskeptic","linkedin":"https://www.linkedin.com/in/kyle-polich-5047193","bio":"Kyle studied computer science and focused on artificial intelligence in grad school. His general interests range from obvious areas like statistics, machine learning, data viz, and optimization to data provenance, data governance, econometrics, and metrology.","sort-rank":1},"last_rendered":"2016-11-19","publish_date":"2015-05-15"},"loaded":1,"content":"

Z-Scores

\n\n

This week's episode dicusses z-scores, also known as standard score. This score describes the distance (in standard deviations)\nthat an observation is away from the mean of the population. A closely related top is the\n68-95-99.7 rule which tells us that (approximately) 68% of a normally\ndistributed population lies within one standard deviation of the mean, 95 within 2, and 99.7 within 3.

\n\n

Kyle and Linh Da discuss z-scores in the context of human height. If you'd like to calculate your own z-score for height, you can do so below.\nThey further discuss how a z-score can also describe the likelihood that some statistical result is due to chance. Thus, if the significance of\na finding can be said to be $3 \\sigma$, that means that it's 99.7% likely not due to chance, or only 0.3% likely to be due to chance.

\n\n
\"plot
\n\n

Calculate the z-score of your height

\n\n\n\nMale\nFemale\n\n

\n\nAssumed standard deviation:\n inches\n\n

\n\n\n\t\n\t\t\n\t\t\n\t\n
Ft:\n\t\t\t\n\t\tInches:\n\t\t\t\n\t\t
\n\n
\n\n\n\t\n\t\t\n\t\t\n\t\n\t\n\t\t\n\t\t\n\t\n
z-score:
percentile:
\n\n
\n\n

Using the interface above, select your gender and enter your height, and your z-score will be calculated.\nAdditionally, this page reports the percentile - the percentage of the population which is shorter than you.

\n\n

\n\n\n \n\n","pathname":"/blog/episodes/2015/z-scores","contributor":{"prettyname":"Kyle Polich","img":"https://s3.amazonaws.com/dataskeptic.com/contributors/kyle-polich.png","twitter":"@dataskeptic","linkedin":"https://www.linkedin.com/in/kyle-polich-5047193","bio":"Kyle studied computer science and focused on artificial intelligence in grad school. His general interests range from obvious areas like statistics, machine learning, data viz, and optimization to data provenance, data governance, econometrics, and metrology.","sort-rank":1}},"postLoading":false},"cart":{"invalid_submit":false,"cart_items":[],"paymentError":"","total":0,"country_long":"United States of America","invoice":{"submitDisabled":false,"paymentError":"","paymentComplete":false},"shipping":0,"stripeLoading":false,"go_to_checkout":false,"cart_visible":false,"country_short":"us","submitDisabled":false,"address":{"zip":"","city":"","phone":"","state":"","last_name":"","street_1":"","street_2":"","first_name":"","email":""},"prod":true,"focus":"first_name","token":null,"stripeLoadingError":false,"focus_msg":"","sizeSelected":{},"paymentComplete":false},"episodes":{"episodes":[],"loaded":false,"years":["2017","2016","2015","2014"],"focus_episode":{"episode":{"img":"https://static.libsyn.com/p/assets/e/1/9/b/e19bf100b7f7e8b5/z-scores.png","num":54,"guid":"6f45383e523b8da393b058122b8b247c","pubDate":"2015-05-15T05:08:03.000Z","mp3":"http://traffic.libsyn.com/dataskeptic/z-scores.mp3?dest-id=201630","desc":"

This week's episode dicusses z-scores, also known as standard score. This score describes the distance (in standard deviations) that an observation is away from the mean of the population. A closely related top is the 68-95-99.7 rule which tells us that (approximately) 68% of a normally distributed population lies within one standard deviation of the mean, 95 within 2, and 99.7 within 3.

\r\n

Kyle and Linh Da discuss z-scores in the context of human height. If you'd like to calculate your own z-score for height, you can do so below. They further discuss how a z-score can also describe the likelihood that some statistical result is due to chance. Thus, if the significance of a finding can be said to be 3σ, that means that it's 99.7% likely not due to chance, or only 0.3% likely to be due to chance.

","duration":"10:26","title":"[MINI] z-scores","link":"http://dataskeptic.com/epnotes/ep54_z-scores.php"},"loaded":1}},"advertise":{"card":"
\n\t
\n\t\t
\n\t\t
\n\t\t

\n\t\t

Thanks to Periscope Data for sponsoring this week's episode of Data Skeptic.

\n\t\t

Please visit https://www.periscopedata.com/skeptics
\n\t\tto learn more about what you can do with their tools.

\n\t
\n
\n\n","banner":null},"products":{"products":[],"products_loaded":0},"player":{"is_playing":false,"has_shown":false,"playback_loaded":false,"position":0,"position_updated":false,"episode":{}},"contributors":{"list":{"kyle":{"prettyname":"Kyle Polich","img":"https://s3.amazonaws.com/dataskeptic.com/contributors/kyle-polich.png","twitter":"@dataskeptic","linkedin":"https://www.linkedin.com/in/kyle-polich-5047193","bio":"Kyle studied computer science and focused on artificial intelligence in grad school. His general interests range from obvious areas like statistics, machine learning, data viz, and optimization to data provenance, data governance, econometrics, and metrology.","sort-rank":1},"linhda":{"prettyname":"Linh Da Tran","img":"","twitter":"","bio":"Originally from North Carolina, Linhda graduated undergrad from UNC-Chapel Hill (Tarheels!) and promptly moved to the Golden Coast when she heard of sunnier days, fewer mosquitos and a long coastline of beaches. When she is not on the podcast, she enjoys commuting to work via bike, spending time with Yoshi, cooking then eating, lots of sleep and occasional yoga and making small-batch artisan ice cream. Her short stature and below average bike size has deterred many a LA bike thieves-- evidence that it pay off to be short.","sort-rank":1},"yoshi":{"prettyname":"Yoshi","img":"https://s3.amazonaws.com/dataskeptic.com/contributors/yoshi.gif","twitter":"","bio":"This Lilac-Crowned Amazon is mostly green all over with a patch if lilac and red on her forward. Her vocabulary ranges from \"I love you\" to \"you're a good girl, Yoshi!\" Pastimes include enjoying a misty bath time on the front patio, chewing on pieces of wood, and making a mess by flinging her dinner everywhere. In addition to being a frequent background commentator, she also inspires many of the topics for our mini-episodes.","sort-rank":1},"megan":{"prettyname":"Megan Ray Nichols","img":"https://s3.amazonaws.com/dataskeptic.com/contributors/megan-ray-nichols.jpg","twitter":"nicholsrmegan","bio":"[Megan Ray Nichols](https://about.me/megan-ray-nichols) is a freelance science writer and the editor of Schooled By Science, a blog dedicated to making science understandable to those without a science degree. She is also a regular contributor to The Energy Collective, Datafloq and Vision Times. Subscribe to [Schooled By Science](http://schooledbyscience.com/subscribe/) for the latest news and follow Megan on [Twitter](https://twitter.com/nicholsrmegan).","sort-rank":2},"jack-simpson":{"prettyname":"Jack Simpson","img":"https://s3.amazonaws.com/dataskeptic.com/contributors/jack-simpson.jpg","twitter":"jack_simpson","linkedin":"https://au.linkedin.com/in/jackbrucesimpson","bio":"Jack Simpson is completing a PhD in computational biology at the Australian National University in 2017. Over the course of his PhD, Jack has gained a keen interest in how machine learning can be used to solve problems in both research and industry. Jack is also passionate about science, programming and beekeeping. His personal blog can be found at [jacksimpson.co](http://www.jacksimpson.co). He also blogs about medical research on [biosky.co](http://biosky.co).","sort-rank":2},"kristine":{"prettyname":"Kristine de Leon","img":"https://s3.amazonaws.com/dataskeptic.com/contributors/kristen-de-leon.png","twitter":"deleonkrist","linkedin":"https://www.linkedin.com/in/kristine-de-leon-a7544149","bio":"Kristine is a fledgling science writer based in sunny Los Angeles, CA. Once a researcher in soil microbiology, Kristine is passionate about translating science into thrilling stories for all. She enjoys reading, the great outdoors, playing with logical systems, learning how stuff in the world works, and making things with metal.","sort-rank":2},"christine":{"prettyname":"Christine Zhang","img":"https://s3.amazonaws.com/dataskeptic.com/contributors/christine-zhang.png","twitter":"christinezhang","linkedin":"https://www.linkedin.com/in/christineyzhang/","bio":"Christine Zhang is a freelance journalist and data analyst who loves stats, stories, spreadsheets, and sandwiches. She was a 2016 OpenNews fellow at the Los Angeles Times Data Desk and has previously worked at the Brookings Institution in Washington, D.C.","sort-rank":2}}},"site":{"title":"Data Skeptic - The intersection of data science, artificial intelligence, machine learning, statistics, and scientific skepticism","disqus_username":"dataskeptic","contact_form":{"name":"","email":"","msg":"","error":"","send":"no"},"contributors":{},"slackstatus":"","schemaVersion":"v571"},"admin":{"body":"","from":"orders@dataskeptic.com","subject":"Hello from Data Skeptic","email_send_msg":"","order":{"size":"","color":"Black","customerName":"","quantity":"1","city":"","spError":"","step":"init","zipcode":"","state":"","country":"US","errorMsg":"","designId":"58196cb41338d457459d579c","address1":"","address2":""},"send_headers":"1","templates":[{"name":"Order confirmation","subject":"dataskeptic.com - order confirmed","body":"Hi {name},\n\nWe wanted to let you know that your order has processed and we'll send another confirmation shortly when it ships.\n\nThanks for your support,\n\nThe Data Skeptic team"},{"name":"Order shipped","subject":"dataskeptic.com - order shipped","body":"Hi {name},\n\nWe wanted to let you know that your recent order has shipped.\n\nThanks for your support,\n\nThe Data Skeptic team"},{"name":"Coaching renewing","subject":"dataskeptic.com - reminder of upcoming charge","body":"Hi {name},\n\nWe wanted to let you know that your monthly coaching plan will recur on {date}. 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