9 ms·

> It compared adulthood life outcomes at age 35, including alcohol use, tobacco smoking, illicit drug use, relationship status, financial hardship, depression,

> It compared adulthood life outcomes at age 35, including alcohol use, tobacco smoking, illicit drug use, relationship status, financial hardship, depression, anxiety and employment status. As always, remember that correlation is not causation. For example, maybe people with depression or anxiety are using cannabis to self-medicate.

joe-collins 6y ago
Additionally: > The research project followed 1792 Australian high school students aged 15 in 1992, investigating patterns of cannabis use across 20 years. So, mix in the confounding factor of it not being legal during those years.
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saalweachter 6y ago
Keep in mind that correlation doesn't prove causation, but it does go along with it. Any time you find correlation, something goes along with it. Maybe A causes B. Maybe B causes A. Maybe A and B have a common cause C. Each of those is interesting, even if you don't know which is which from just the correlation. Consider if the data had fallen out the other way: if there was no correlation between these measures of harm and cannabis use, that would have been evidence of a lack of harm from cannabis use. So even if you can't draw any strong conclusions from this study, it's still an interesting data point.
- pintxo 6y ago
  It might also just be a coincidence like the correlation of the "Per capita consumption of cheese (US)" with the "Number of people who died by becoming tangled in their bedsheets" [1] [1] http://tylervigen.com/view_correlation?id=7 http://tylervigen.com/view_correlation?id=7
  - saalweachter 6y ago
    Eh, correlating two time-series trends is almost a gimmick. If you saw that the per-capita cheese consumption of people who died by becoming tangled in their bedsheets was twice or ten times that of the baseline population, that could still be a coincidence, because hey, that's always a possibility, but it would be a much more suggestive correlation than just observing that any two monotonic time series correlate.
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joe-collins 6y ago

Additionally: > The research project followed 1792 Australian high school students aged 15 in 1992, investigating patterns of cannabis use across 20 years. So, mix in the confounding factor of it not being legal during those years.

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saalweachter 6y ago

Keep in mind that correlation doesn't prove causation, but it does go along with it. Any time you find correlation, something goes along with it. Maybe A causes B. Maybe B causes A. Maybe A and B have a common cause C. Each of those is interesting, even if you don't know which is which from just the correlation. Consider if the data had fallen out the other way: if there was no correlation between these measures of harm and cannabis use, that would have been evidence of a lack of harm from cannabis use. So even if you can't draw any strong conclusions from this study, it's still an interesting data point.

pintxo 6y ago
It might also just be a coincidence like the correlation of the "Per capita consumption of cheese (US)" with the "Number of people who died by becoming tangled in their bedsheets" [1] [1] http://tylervigen.com/view_correlation?id=7 http://tylervigen.com/view_correlation?id=7
- saalweachter 6y ago
  Eh, correlating two time-series trends is almost a gimmick. If you saw that the per-capita cheese consumption of people who died by becoming tangled in their bedsheets was twice or ten times that of the baseline population, that could still be a coincidence, because hey, that's always a possibility, but it would be a much more suggestive correlation than just observing that any two monotonic time series correlate.

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