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Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36 ArchiveBox/{VERSION} (+https://github.com/ArchiveBox/ArchiveBox/)", "https://tylervigen.com/spurious-correlations" ], "cmd_version": "8.10.1", "end_ts": "2025-12-18T12:41:36.371423+00:00", "index_texts": null, "output": "headers.json", "pwd": "/data/archive/1766061685.595978", "schema": "ArchiveResult", "start_ts": "2025-12-18T12:41:36.149635+00:00", "status": "succeeded" } ], "htmltotext": [ { "cmd": [ "(internal) archivebox.extractors.htmltotext", "./{singlefile,dom}.html" ], "cmd_version": "0.8.5rc51", "end_ts": "2025-12-18T12:42:33.329733+00:00", "index_texts": [ "Spurious Correlations (favicon.png) (styles/_stylesheet_H01.css) (https://tylervigen.com) (about) about \u00b7 (mailto:emailme@tylervigen.com) email me \u00b7 (subscribe) subscribe (spurious-correlations) spurious correlations correlation is not causation (spurious/random) random \u00b7 (spurious/discover) discover \u00b7 (?page=2) next page \u2192 don't miss (https://tylervigen.com/spurious-scholar) spurious scholar ,where each of these is an academic paper (spurious/correlation/1781_bachelors-degrees-awarded-in-psychology_correlates-with_the-number-of-groundskeepers-in-utah) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Bachelor's degrees awarded in Psychology and the second variable is The number of groundskeepers in Utah. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Bachelor's degrees awarded in Psychology and the second variable is The number of groundskeepers in Utah. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.) (Small Image) View details about correlation #1,781 Show scatterplot What else correlates? (spurious/variable?id=1290) Bachelor's degrees awarded in Psychology \u00b7 (spurious/view-all-variables/education) all education (spurious/variable?id=15091) The number of groundskeepers in Utah \u00b7 (spurious/view-all-variables/occupations) all cccupations (spurious/correlation/1184_the-distance-between-uranus-and-earth_correlates-with_number-of-slot-machines-in-nevada) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The distance between Uranus and Earth and the second variable is Number of Slot Machines in Nevada. The chart goes from 1984 to 2022, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The distance between Uranus and Earth and the second variable is Number of Slot Machines in Nevada. The chart goes from 1984 to 2022, and the two variables track closely in value over that time.) (Small Image) View details about correlation #1,184 Show scatterplot What else correlates? (spurious/variable?id=1943) The distance between Uranus and Earth \u00b7 (spurious/view-all-variables/planets) all planets (spurious/variable?id=2) Number of Slot Machines in Nevada \u00b7 (spurious/view-all-variables/weirdwacky) all weird & wacky (spurious/correlation/5138_masters-degrees-awarded-in-liberal-arts_correlates-with_popularity-of-the-success-kid-meme) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Master's degrees awarded in Liberal arts and the second variable is Popularity of the 'success kid' meme. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Master's degrees awarded in Liberal arts and the second variable is Popularity of the 'success kid' meme. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.) (Small Image) View details about correlation #5,138 Show scatterplot What else correlates? (spurious/variable?id=1328) Master's degrees awarded in Liberal arts \u00b7 (spurious/view-all-variables/education) all education (spurious/variable?id=25157) Popularity of the 'success kid' meme \u00b7 (spurious/view-all-variables/memes) all memes (spurious/correlation/4640_how-nerdy-be-smart-youtube-video-titles-are_correlates-with_the-number-of-film-and-video-editors-in-puerto-rico) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is How nerdy 'Be Smart' science YouTube video titles are and the second variable is The number of film and video editors in Puerto Rico. The chart goes from 2013 to 2022, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is How nerdy 'Be Smart' science YouTube video titles are and the second variable is The number of film and video editors in Puerto Rico. The chart goes from 2013 to 2022, and the two variables track closely in value over that time.) (Small Image) View details about correlation #4,640 Show scatterplot What else correlates? (spurious/variable?id=25857) How nerdy 'Be Smart' science YouTube video titles are \u00b7 (spurious/view-all-variables/youtube) all YouTube (spurious/variable?id=16707) The number of film and video editors in Puerto Rico \u00b7 (spurious/view-all-variables/occupations) all cccupations (spurious/correlation/2194_gmo-use-in-corn-grown-in-ohio_correlates-with_google-searches-for-i-cant-even) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in corn grown in Ohio and the second variable is Google searches for 'i cant even'. The chart goes from 2004 to 2023, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in corn grown in Ohio and the second variable is Google searches for 'i cant even'. The chart goes from 2004 to 2023, and the two variables track closely in value over that time.) (Small Image) View details about correlation #2,194 Show scatterplot What else correlates? (spurious/variable?id=762) GMO use in corn grown in Ohio \u00b7 (spurious/view-all-variables/farmingfood) all food (spurious/variable?id=1525) Google searches for 'i cant even' \u00b7 (spurious/view-all-variables/google) all google searches (spurious/correlation/1082_googles-net-income_correlates-with_sales-of-lpvinyl-albums) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Google's Net Income and the second variable is Sales of LP/Vinyl Albums. The chart goes from 2004 to 2022, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Google's Net Income and the second variable is Sales of LP/Vinyl Albums. The chart goes from 2004 to 2022, and the two variables track closely in value over that time.) (Small Image) View details about correlation #1,082 Show scatterplot What else correlates? (spurious/variable?id=510) Google's Net Income \u00b7 (spurious/view-all-variables/stocks) all stocks (spurious/variable?id=25) Sales of LP/Vinyl Albums \u00b7 (spurious/view-all-variables/weirdwacky) all weird & wacky (spurious/correlation/5024_popularity-of-the-first-name-thomas_correlates-with_gasoline-pumped-in-france) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Thomas and the second variable is Gasoline pumped in France. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Thomas and the second variable is Gasoline pumped in France. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.) (Small Image) View details about correlation #5,024 Show scatterplot What else correlates? (spurious/variable?id=2013) Popularity of the first name Thomas \u00b7 (spurious/view-all-variables/babynames) all first names (spurious/variable?id=24425) Gasoline pumped in France \u00b7 (spurious/view-all-variables/energy) all energy (spurious/correlation/5878_how-geeky-asapscience-youtube-video-titles-are_correlates-with_the-number-of-movies-hugh-jackman-appeared-in) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is How geeky AsapSCIENCE YouTube video titles are and the second variable is The number of movies Hugh Jackman appeared in. The chart goes from 2012 to 2023, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is How geeky AsapSCIENCE YouTube video titles are and the second variable is The number of movies Hugh Jackman appeared in. The chart goes from 2012 to 2023, and the two variables track closely in value over that time.) (Small Image) View details about correlation #5,878 Show scatterplot What else correlates? (spurious/variable?id=25844) How geeky AsapSCIENCE YouTube video titles are \u00b7 (spurious/view-all-variables/youtube) all YouTube (spurious/variable?id=26489) The number of movies Hugh Jackman appeared in \u00b7 (spurious/view-all-variables/films) all films & actors (spurious/correlation/2354_bachelors-degrees-awarded-in-engineering_correlates-with_google-searches-for-dollar-store-near-me) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Bachelor's degrees awarded in Engineering and the second variable is Google searches for 'dollar store near me'. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Bachelor's degrees awarded in Engineering and the second variable is Google searches for 'dollar store near me'. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.) (Small Image) View details about correlation #2,354 Show scatterplot What else correlates? (spurious/variable?id=1273) Bachelor's degrees awarded in Engineering \u00b7 (spurious/view-all-variables/education) all education (spurious/variable?id=1375) Google searches for 'dollar store near me' \u00b7 (spurious/view-all-variables/google) all google searches (spurious/correlation/5943_the-number-of-secretaries-in-rhode-island_correlates-with_customer-satisfaction-with-ups) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of secretaries in Rhode Island and the second variable is Customer satisfaction with UPS. The chart goes from 2010 to 2021, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of secretaries in Rhode Island and the second variable is Customer satisfaction with UPS. The chart goes from 2010 to 2021, and the two variables track closely in value over that time.) (Small Image) View details about correlation #5,943 Show scatterplot What else correlates? (spurious/variable?id=18577) The number of secretaries in Rhode Island \u00b7 (spurious/view-all-variables/occupations) all cccupations (spurious/variable?id=19839) Customer satisfaction with UPS \u00b7 (spurious/view-all-variables/weirdwacky) all weird & wacky (spurious/correlation/1739_popularity-of-the-first-name-jamila_correlates-with_good-air-quality-in-phoenix) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Jamila and the second variable is Air quality in Phoenix. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Jamila and the second variable is Air quality in Phoenix. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.) (Small Image) View details about correlation #1,739 Show scatterplot What else correlates? (spurious/variable?id=3961) Popularity of the first name Jamila \u00b7 (spurious/view-all-variables/babynames) all first names (spurious/variable?id=21062) Air quality in Phoenix \u00b7 (spurious/view-all-variables/weather) all weather (spurious/correlation/4113_liquefied-petroleum-gas-used-in-bahrain_correlates-with_mizuho-financial-groups-stock-price) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Liquefied petroleum gas used in Bahrain and the second variable is Mizuho Financial Group's stock price (MFG). The chart goes from 2007 to 2021, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Liquefied petroleum gas used in Bahrain and the second variable is Mizuho Financial Group's stock price (MFG). The chart goes from 2007 to 2021, and the two variables track closely in value over that time.) (Small Image) View details about correlation #4,113 Show scatterplot What else correlates? (spurious/variable?id=24174) Liquefied petroleum gas used in Bahrain \u00b7 (spurious/view-all-variables/energy) all energy (spurious/variable?id=1751) Mizuho Financial Group's stock price (MFG) \u00b7 (spurious/view-all-variables/stocks) all stocks (spurious/correlation/1199_american-cheese-consumption_correlates-with_googles-annual-global-revenue) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is American cheese consumption and the second variable is Google's Annual Global Revenue. The chart goes from 2002 to 2021, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is American cheese consumption and the second variable is Google's Annual Global Revenue. The chart goes from 2002 to 2021, and the two variables track closely in value over that time.) (Small Image) View details about correlation #1,199 Show scatterplot What else correlates? (spurious/variable?id=553) American cheese consumption \u00b7 (spurious/view-all-variables/farmingfood) all food (spurious/variable?id=117) Google's Annual Global Revenue \u00b7 (spurious/view-all-variables/weirdwacky) all weird & wacky (spurious/correlation/5949_air-pollution-in-san-diego-california_correlates-with_popularity-of-the-first-name-kirk) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in San Diego, California and the second variable is Popularity of the first name Kirk. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in San Diego, California and the second variable is Popularity of the first name Kirk. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.) (Small Image) View details about correlation #5,949 Show scatterplot What else correlates? (spurious/variable?id=21206) Air pollution in San Diego, California \u00b7 (spurious/view-all-variables/weather) all weather (spurious/variable?id=3259) Popularity of the first name Kirk \u00b7 (spurious/view-all-variables/babynames) all first names (spurious/correlation/1059_petroluem-consumption-in-azerbaijan_correlates-with_the-number-of-farm-equipment-mechanics-in-alabama) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Petroluem consumption in Azerbaijan and the second variable is The number of farm equipment mechanics in Alabama. The chart goes from 2010 to 2021, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Petroluem consumption in Azerbaijan and the second variable is The number of farm equipment mechanics in Alabama. The chart goes from 2010 to 2021, and the two variables track closely in value over that time.) (Small Image) View details about correlation #1,059 Show scatterplot What else correlates? (spurious/variable?id=24166) Petroluem consumption in Azerbaijan \u00b7 (spurious/view-all-variables/energy) all energy (spurious/variable?id=17788) The number of farm equipment mechanics in Alabama \u00b7 (spurious/view-all-variables/occupations) all cccupations (spurious/correlation/2163_masters-degrees-awarded-in-education_correlates-with_google-searches-for-gangnam-style) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Master's degrees awarded in Education and the second variable is Google searches for 'Gangnam Style'. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Master's degrees awarded in Education and the second variable is Google searches for 'Gangnam Style'. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.) (Small Image) View details about correlation #2,163 Show scatterplot What else correlates? (spurious/variable?id=1319) Master's degrees awarded in Education \u00b7 (spurious/view-all-variables/education) all education (spurious/variable?id=1442) Google searches for 'Gangnam Style' \u00b7 (spurious/view-all-variables/google) all google searches (spurious/correlation/1746_gmo-use-in-cotton-in-texas_correlates-with_pirate-attacks-globally) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in cotton in Texas and the second variable is Pirate attacks globally. The chart goes from 2009 to 2022, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in cotton in Texas and the second variable is Pirate attacks globally. The chart goes from 2009 to 2022, and the two variables track closely in value over that time.) (Small Image) View details about correlation #1,746 Show scatterplot What else correlates? (spurious/variable?id=776) GMO use in cotton in Texas \u00b7 (spurious/view-all-variables/farmingfood) all food (spurious/variable?id=488) Pirate attacks globally \u00b7 (spurious/view-all-variables/weirdwacky) all weird & wacky (spurious/correlation/2636_the-distance-between-neptune-and-uranus_correlates-with_cognizant-technology-solutions-stock-price) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The distance between Neptune and Uranus and the second variable is Cognizant Technology Solutions' stock price (CTSH). The chart goes from 2002 to 2023, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The distance between Neptune and Uranus and the second variable is Cognizant Technology Solutions' stock price (CTSH). The chart goes from 2002 to 2023, and the two variables track closely in value over that time.) (Small Image) View details about correlation #2,636 Show scatterplot What else correlates? (spurious/variable?id=1967) The distance between Neptune and Uranus \u00b7 (spurious/view-all-variables/planets) all planets (spurious/variable?id=1807) Cognizant Technology Solutions' stock price (CTSH) \u00b7 (spurious/view-all-variables/stocks) all stocks (spurious/correlation/3596_popularity-of-the-first-name-coral_correlates-with_the-number-of-biological-technicians-in-missouri) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Coral and the second variable is The number of biological technicians in Missouri. The chart goes from 2003 to 2020, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Coral and the second variable is The number of biological technicians in Missouri. The chart goes from 2003 to 2020, and the two variables track closely in value over that time.) (Small Image) View details about correlation #3,596 Show scatterplot What else correlates? (spurious/variable?id=3812) Popularity of the first name Coral \u00b7 (spurious/view-all-variables/babynames) all first names (spurious/variable?id=10419) The number of biological technicians in Missouri \u00b7 (spurious/view-all-variables/occupations) all cccupations (spurious/correlation/1779_masters-degrees-awarded-in-homeland-security-law-enforcement-and-firefighting_correlates-with_google-searches-for-ice-bath) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Master's degrees awarded in law enforcement and firefighting and the second variable is Google searches for 'ice bath'. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Master's degrees awarded in law enforcement and firefighting and the second variable is Google searches for 'ice bath'. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.) (Small Image) View details about correlation #1,779 Show scatterplot What else correlates? (spurious/variable?id=1326) Master's degrees awarded in law enforcement and firefighting \u00b7 (spurious/view-all-variables/education) all education (spurious/variable?id=1408) Google searches for 'ice bath' \u00b7 (spurious/view-all-variables/google) all google searches (spurious/correlation/5920_per-capita-consumption-of-margarine_correlates-with_the-divorce-rate-in-maine) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Per capita consumption of margarine and the second variable is The divorce rate in Maine. The chart goes from 2000 to 2009, and the two variables track closely in value over that time.) (A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Per capita consumption of margarine and the second variable is The divorce rate in Maine. The chart goes from 2000 to 2009, and the two variables track closely in value over that time.) (Small Image) View details about correlation #5,920 Show scatterplot What else correlates? (spurious/variable?id=26741) Per capita consumption of margarine \u00b7 (spurious/view-all-variables/farmingfood) all food (spurious/variable?id=19802) The divorce rate in Maine \u00b7 (spurious/view-all-variables/statespecific) all random state specific (?page=2) next page \u2192 (spurious/discover) Discover a new correlation (spurious/random) View random correlation (spurious/view-all-correlations) View all correlations (spurious/spurious-research-papers) View all research papers (permission) Get permission to re-use these charts Why this works Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 correlation calculations! This is called \u201c(https://en.wikipedia.org/wiki/Data_dredging) data dredging .\u201dNote Fun fact: the chart used on the wikipedia page to demonstrate data dredging is also from me. I've been being naughty with data since 2014. Instead of starting with a hypothesis and testing it, I instead tossed a bunch of data in a blender to see what correlations would shake out. It\u2019s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random. Lack of causal connection: There is probably no direct connection between these variables, despite what the AI says above.Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole. This is exacerbated by the fact that I used \"Years\" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like \"one person\" in stead of \"one year\" to be the \"thing\" studied. Observations not independent: For many variables, sequential years are not independent of each other. You will often see trend-lines form. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A naive p -value calculation does not take this into account.Note You will calculate a lower chance of \"randomly\" achieving the result than represents reality.To be more specific: p-value tests are probability values, where you are calculating the probability of achieving a result at least as extreme as you found completely by chance. When calculating a p-value, you need to assert how many \"degrees of freedom\" your variable has. I count each year (minus one) as a \"degree of freedom,\" but this is misleading for continuous variables.This kind of thing can creep up on you pretty easily when using p-values, which is why it's best to take it as \"one of many\" inputs that help you assess the results of your analysis. Y-axes doesn't start at zero: I truncated the Y-axes of the graphs above. I also used a line graph, which makes the visual connection stand out more than it deserves. Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself! Mathematically what I showed is true, but it is intentionally misleading. If you click on any of the charts that abuse this, you can scroll down to see a version that starts at zero. Confounding variable: Confounding variables (like global pandemics) will cause two variables to look connected when in fact a \"sneaky third\" variable is influencing both of them behind the scenes. Outliers: Some datasets here have outliers which drag up the correlation.Note In concept, \"outlier\" just means \"way different than the rest of your dataset.\" When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.Because this page is automatically generated, I don't know whether any of the charts displayed on it have outliers. I'm just a footnote. \u00af\\_(\u30c4)_/\u00af I intentionally mishandeled outliers, which makes the correlation look extra strong. Low n : There are not many data points included in some of these charts.Note You can do analyses with low ns! But you shouldn't data dredge with a low n. Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation. Pro-tip: click on any correlation to see : Detailed data sources Prompts for the AI-generated content Explanations of each of the calculations (correlation, p-value) Python code to calculate it yourself Project by (https://www.linkedin.com/in/tyler-vigen/) Tyler Vigen (mailto:emailme@tylervigen.com) emailme@tylervigen.com \u00b7 (about) about \u00b7 (subscribe) subscribe (http://creativecommons.org/licenses/by/4.0/) CC BY 4.0 " ], "output": "htmltotext.txt", "pwd": "/data/archive/1766061685.595978", "schema": "ArchiveResult", "start_ts": "2025-12-18T12:42:33.205041+00:00", "status": "succeeded" } ], "media": [ { "cmd": [ "/usr/local/bin/yt-dlp", "--restrict-filenames", "--trim-filenames", "128", "--write-description", "--write-info-json", "--write-annotations", "--write-thumbnail", "--no-call-home", "--write-sub", "--write-auto-subs", "--convert-subs=srt", "--yes-playlist", "--continue", "--no-abort-on-error", "--ignore-errors", "--geo-bypass", "--add-metadata", "--format=(bv*+ba/b)[filesize<=750m][filesize_approx<=?750m]/(bv*+ba/b)", "--skip-download", "--cache-dir=/data/yt-dlp-cache/", "--cookies=/data/yt-dlp-cache/cookies.txt", "--proxy=socks5://tor-socks-proxy:9150", "--no-playlist", "https://tylervigen.com/spurious-correlations" ], "cmd_version": "2024.10.7", "end_ts": "2025-12-18T12:42:42.768618+00:00", "index_texts": [], "output": "media/", "pwd": "/data/archive/1766061685.595978", "schema": "ArchiveResult", "start_ts": "2025-12-18T12:42:37.535868+00:00", "status": "succeeded" } ], "mercury": [ { "cmd": [ "/home/archivebox/.npm/bin/postlight-parser", "https://tylervigen.com/spurious-correlations" ], "cmd_version": "2.2.3", "end_ts": "2025-12-18T12:42:32.714881+00:00", "index_texts": null, "output": "mercury/", "pwd": "/data/archive/1766061685.595978", "schema": "ArchiveResult", "start_ts": "2025-12-18T12:42:12.687145+00:00", "status": "succeeded" } ], "pdf": [], "readability": [ { "cmd": [ "/home/archivebox/.npm/bin/readability-extractor", "/tmp/tmpy_b74ta7", "https://tylervigen.com/spurious-correlations" ], "cmd_version": "0.0.11", "end_ts": "2025-12-18T12:41:59.933004+00:00", "index_texts": [ "spurious correlations\ncorrelation is not causation\nrandom \u00b7 discover \u00b7 next page \u2192 \ndon't miss spurious scholar,where each of these is an academic paper\n\n\n\n \n \n \n\n\nView details about correlation #1,781\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #1,184\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #5,138\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #4,640\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #2,194\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #1,082\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #5,024\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #5,878\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #2,354\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #5,943\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #1,739\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #4,113\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #1,199\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #5,949\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #1,059\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #2,163\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #1,746\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #2,636\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #3,596\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #1,779\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n \n \n\n\nView details about correlation #5,920\n\n\n\nShow scatterplot\n\n\n\n\n\n\n\n \n \n \n \n\nnext page \u2192 \n\n\n\nWhy this works\n\n\n\n\n \n\nData dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 correlation calculations! This is called \u201cdata dredging.\u201dNote\n\nFun fact: the chart used on the wikipedia page to demonstrate data dredging is also from me. I've been being naughty with data since 2014. \n Instead of starting with a hypothesis and testing it, I instead tossed a bunch of data in a blender to see what correlations would shake out. It\u2019s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.\nLack of causal connection: There is probably no direct connection between these variables, despite what the AI says above.Note\n\nBecause these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.\n This is exacerbated by the fact that I used \"Years\" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like \"one person\" in stead of \"one year\" to be the \"thing\" studied.\n\nObservations not independent: For many variables, sequential years are not independent of each other. You will often see trend-lines form. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A naive p-value calculation does not take this into account.Note\n\n You will calculate a lower chance of \"randomly\" achieving the result than represents reality.\nTo be more specific: p-value tests are probability values, where you are calculating the probability of achieving a result at least as extreme as you found completely by chance. When calculating a p-value, you need to assert how many \"degrees of freedom\" your variable has. I count each year (minus one) as a \"degree of freedom,\" but this is misleading for continuous variables.This kind of thing can creep up on you pretty easily when using p-values, which is why it's best to take it as \"one of many\" inputs that help you assess the results of your analysis.\n\n\n\n\nY-axes doesn't start at zero: I truncated the Y-axes of the graphs above. I also used a line graph, which makes the visual connection stand out more than it deserves.\nNote\n\nNothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!\n \n Mathematically what I showed is true, but it is intentionally misleading. If you click on any of the charts that abuse this, you can scroll down to see a version that starts at zero. \n\nConfounding variable: Confounding variables (like global pandemics) will cause two variables to look connected when in fact a \"sneaky third\" variable is influencing both of them behind the scenes.\n\n\nOutliers: Some datasets here have outliers which drag up the correlation.Note\n\nIn concept, \"outlier\" just means \"way different than the rest of your dataset.\" When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.\nBecause this page is automatically generated, I don't know whether any of the charts displayed on it have outliers. I'm just a footnote. \u00af\\_(\u30c4)_/\u00af\n I intentionally mishandeled outliers, which makes the correlation look extra strong.\n\n\nLow n: There are not many data points included in some of these charts.Note\n\nYou can do analyses with low ns! But you shouldn't data dredge with a low n.\n Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.\n\n\n\n\n\n\n\n\n\nPro-tip: click on any correlation to see:\n\n Detailed data sources\n Prompts for the AI-generated content\n Explanations of each of the calculations (correlation, p-value)\n Python code to calculate it yourself" ], "output": "readability/", "pwd": "/data/archive/1766061685.595978", "schema": "ArchiveResult", "start_ts": "2025-12-18T12:41:55.659993+00:00", "status": "succeeded" } ], "screenshot": [], "singlefile": [], "title": [ { "cmd": [ "/usr/bin/curl", "--silent", "--location", "--compressed", "--proxy", "socks5://tor-socks-proxy:9150", "--max-time", "60", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36 ArchiveBox/{VERSION} (+https://github.com/ArchiveBox/ArchiveBox/)", "https://tylervigen.com/spurious-correlations" ], "cmd_version": "8.10.1", "end_ts": "2025-12-18T12:41:54.982405+00:00", "index_texts": null, "output": "Spurious Correlations", "pwd": "/data/archive/1766061685.595978", "schema": "ArchiveResult", "start_ts": "2025-12-18T12:41:54.935868+00:00", "status": "succeeded" } ], "wget": [] }, "icons": null, "is_archived": true, "is_static": false, "latest": { "archive_org": "TimeoutExpired: Command '['/usr/bin/curl', '--silent', '--location', '--compressed', '--proxy', 'socks5://tor-socks-proxy:9150', '--head', '--max-time', '60', '--user-agent', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36 ArchiveBox/{VERSION} (+https://github.com/ArchiveBox/ArchiveBox/)', 'https://web.archive.org/save/https://tylervigen.com/spurious-correlations']' timed out after 60 seconds", "dom": "output.html", "favicon": "favicon.ico", "git": null, "media": "media/", "pdf": null, "screenshot": null, "singlefile": null, "title": "Spurious Correlations", "warc": null, "wget": null }, "link_dir": "/data/archive/1766061685.595978", "newest_archive_date": "2025-12-18T12:42:42.854700+00:00", "num_failures": 1, "num_outputs": 8, "oldest_archive_date": "2025-12-18T12:41:32.803609+00:00", "path": "/spurious-correlations", "schema": "Link", "scheme": "https", "snapshot_abid": "snp_01KCRQNEY520F810C601ZFGJAB", "snapshot_id": "6a45a384-5782-4170-bed0-e47e3ef8494b", "sources": [ "/data/sources/1766061684-import.txt" ], "tags": null, "tags_str": "", "timestamp": "1766061685.595978", "title": "Spurious Correlations", "url": "https://tylervigen.com/spurious-correlations" }