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Y'all are 100% right – something wonky was going on! I’ve since made adjustments and updated the piece with a correction. The visual and accompanying text were

Y'all are 100% right – something wonky was going on! I’ve since made adjustments and updated the piece with a correction. The visual and accompanying text were using the number of all women plays, not just the back-to-back ones. It’s more likely that the simulation back-to-back numbers would fall between 7—17 a day NOT the originally stated 30–40. For the particular station in the intro, San Antonio (KCYY-FM), the number of women’s back-to-back plays that they actually played isn’t completely outside of the realm of possibilities if left up to chance, but we would be more likely to see higher numbers. For 17 other stations, the observed numbers do fall outside the simulation distribution. (See the histograms in the methodology section. Note that they are for the full 19 days.) Thank y'all again for raising this issue! The data is only good as the humans behind it, and I definitely bungled this a bit.

tzs 3y ago
That's still about twice or so the number that the simulations of sxp and myself give, but I think that we both used a different definition of back-to-back than you. We both are not counting the first song in a run of consecutive same gender artist songs as being back-to-back. If the first song in a run is included in the back-to-back count then my simulation seems to be matching your updated simulations. If 11% of the songs are from female artists for example I get that ~2.27% of the total plays should be part of back-to-back runs of female artists. If the first song in a run does not count as back-to-back, that was ~1.22%. Note that the percentages for "first doesn't count" B2B simulations come out pretty close to the B2B percentages given in the article for most stations. That could just be a coincidence, but it is probably worth double checking how you calculated the B2B percentages for the actual station data to make sure that you are using "first counts" B2B. If the station stats are using "first counts", then it looks like most of the stations are playing about 1/2 as many female artist songs B2B as they would if scheduling were random. But if the station stats are using "first doesn't count", then it looks like most are consistent with random playlists.
- jan_diehm 3y ago
  Looking into this more now.
- jan_diehm 3y ago
  Reporting back again… We’ve checked the overall simulations and they check out when you use the summary data here (https://github.com/the-pudding/country-radio-data/blob/main/output/summary.csv https://github.com/the-pudding/country-radio-data/blob/main/...) for the total number of songs (total_COUNT) and the percentage of women’s songs (onlyWomenSongs_PERCENT) for each station. For San Antonio (KCYY-FM), that would mean that the total observed number of women’s back-to-back songs (64) is within the realm of possibilities across all 19 days if left up to chance. But, the observed number is on the low end of the overall distribution, with the majority of numbers landing between 70-100. I have been able to recreate this using sxp’s original Javascript. When we’re talking about a difference of say 20 songs (across 19 days) you can’t visually see it, which is why the single day example was a poor choice for communicating this, especially because San Antonio (KCYY-FM) is one of the “better” stations. We’ve removed the simulation section from the intro, made it clear what data the simulations use further into the story, and added a correction. Thank you again for keeping us honest.

tzs 3y ago

That's still about twice or so the number that the simulations of sxp and myself give, but I think that we both used a different definition of back-to-back than you. We both are not counting the first song in a run of consecutive same gender artist songs as being back-to-back. If the first song in a run is included in the back-to-back count then my simulation seems to be matching your updated simulations. If 11% of the songs are from female artists for example I get that ~2.27% of the total plays should be part of back-to-back runs of female artists. If the first song in a run does not count as back-to-back, that was ~1.22%. Note that the percentages for "first doesn't count" B2B simulations come out pretty close to the B2B percentages given in the article for most stations. That could just be a coincidence, but it is probably worth double checking how you calculated the B2B percentages for the actual station data to make sure that you are using "first counts" B2B. If the station stats are using "first counts", then it looks like most of the stations are playing about 1/2 as many female artist songs B2B as they would if scheduling were random. But if the station stats are using "first doesn't count", then it looks like most are consistent with random playlists.

jan_diehm 3y ago
Looking into this more now.
jan_diehm 3y ago
Reporting back again… We’ve checked the overall simulations and they check out when you use the summary data here (https://github.com/the-pudding/country-radio-data/blob/main/output/summary.csv https://github.com/the-pudding/country-radio-data/blob/main/...) for the total number of songs (total_COUNT) and the percentage of women’s songs (onlyWomenSongs_PERCENT) for each station. For San Antonio (KCYY-FM), that would mean that the total observed number of women’s back-to-back songs (64) is within the realm of possibilities across all 19 days if left up to chance. But, the observed number is on the low end of the overall distribution, with the majority of numbers landing between 70-100. I have been able to recreate this using sxp’s original Javascript. When we’re talking about a difference of say 20 songs (across 19 days) you can’t visually see it, which is why the single day example was a poor choice for communicating this, especially because San Antonio (KCYY-FM) is one of the “better” stations. We’ve removed the simulation section from the intro, made it clear what data the simulations use further into the story, and added a correction. Thank you again for keeping us honest.