4 ms·

From the abstract of the study: Participants were 7744 men (20-89 yr) initially free of CVD who returned a mail-back survey during 1982. Time spent watching TV

by sdfx 16y ago

From the abstract of the study: Participants were 7744 men (20-89 yr) initially free of CVD who returned a mail-back survey during 1982. Time spent watching TV and time spent riding in a car were reported. Mortality data were ascertained through the National Death Index until December 31, 2003. They noted that they adjusted for age. I presume other factors like the economic status correlate also with the time they watched tv. Don't know if they've adjusted for that as well.

hugh3 16y ago
So they didn't take time spent sitting in an office into account at all? Seems odd. I spend at least two hours sitting at a desk for every hour I spend sitting on a couch or in a car seat.
- joe_the_user 16y ago
  It's interesting indeed. Either they controlled for occupation (doubtful) or sitting in an office didn't matter or the thing whole is just off-base. One way that sitting in an office is different from watching TV involves complete passivity whereas in an office setting, your actively engaged in some task. But driving a car is probably closer to sitting in an office than watching TV. So the whole things seem problematic.
carbocation 16y ago
Right, but my point is not about whether or not they adjusted for the right factors. Whether or not they did that, it is clear that they took a retrospective peek at the data and said, "Aha! If we make cutpoints at 11 hours and 23 hours, we get interesting results." Adjustment for age, which should be done, has no bearing on whether this post hoc slicing of the data to give good results is proper. This is only OK so long as you're intentionally exploring, and then perhaps doing a follow-up study using those cutpoints in prespecified fashion using fresh data. In other words, if you went back and looked at your data and found the best cutoffs to maximize difference in risk, is it any surprise that you found a big difference in risk? This is why they need to validate these cutpoints in a separate study.
- hartror 16y ago
  I'm afraid you use the post hoc fallacy incorrectly. The post hoc fallacy is drawing conclusions based on statistically insignificant data sets, normally anecdotes of size 1, at least in every day life. Now assuming this study was done by half way competent statisticians they would have followed the tried and true methods of establishing statistical significance. Sure they could have made mistakes on the way, the entire study could be completely invalidated by a detail or two. But even in that case it isn't a post hoc ergo propter hoc. Your thinking could stem from an affliction brought on by high schools teaching of science. They really only teach one of the scientific methods, that of hypothesis, experimentation, observation and conclusion in that order. However depending on the field of science and the subject at hand that isn't necessarily the order thing are done in. Science isn't a simple linear process like we are lead to believe in high school.
  - carbocation 16y ago
    I am speaking in the plain language used by those of us who do science for a living. I'm using the term post hoc in the way that we in science usually do (see http://en.wikipedia.org/wiki/Post-hoc_analysis http://en.wikipedia.org/wiki/Post-hoc_analysis if you're not familiar). I'm not sure how high school science teachers fit into this; sounds like you had a bad experience and I'm sorry if so, but that really has nothing to do with this discussion. There is nothing wrong with "my thinking" but you appear to think so because your understanding is incorrect. Post hoc ergo propter hoc is the fallacy of thinking that, because something comes before another, that first something causes the second. I'm not talking about that at all. Post hoc analysis is something totally different - viz, it's performing non-prespecified analyses. It's more closely coupled to multiple testing than it is to the other fallacy you are referring to. It certainly is not limited to small sample sizes or sample sizes of one, in contrast to what you state. You can do science improperly with samples of any size.
    - _delirium 16y ago
      I could be wrong, but at least in my little corner of science, this particular kind of post-hoc analysis, where you're moving thresholds on a linear scale for reporting purposes, seems not to be viewed with great suspicion, because it doesn't really produce arbitrary degrees of freedom. The worse kind of post-hoc analysis is where you can end up claiming things like "men who watched TV between 5.5 and 9 hours a week [whatever] more than men who watched TV either 1.0-2.0 or 11.5-12.5 hours/week", retroactively discovering a million probably random correlations in your data. Though even there there are ways of reducing the risk of doing so; for example, cross-validation is commonly used to test whether discovered correlations are artifacts of sampling or not, and has some pretty solid statistical theory behind it. But in this case the general hypothesis is pre-chosen and fixed, basically "men who watch TV more hours [whatever] more than men who watch TV fewer hours", and the choice of threshholds is more of a reporting step, pulling out some binned tails at either extreme for emphasis and ease of discussion, while the real correlation is full-curve-to-full-curve.

by sdfx 16y ago

hugh3 16y ago

So they didn't take time spent sitting in an office into account at all? Seems odd. I spend at least two hours sitting at a desk for every hour I spend sitting on a couch or in a car seat.

joe_the_user 16y ago
It's interesting indeed. Either they controlled for occupation (doubtful) or sitting in an office didn't matter or the thing whole is just off-base. One way that sitting in an office is different from watching TV involves complete passivity whereas in an office setting, your actively engaged in some task. But driving a car is probably closer to sitting in an office than watching TV. So the whole things seem problematic.

carbocation 16y ago

Right, but my point is not about whether or not they adjusted for the right factors. Whether or not they did that, it is clear that they took a retrospective peek at the data and said, "Aha! If we make cutpoints at 11 hours and 23 hours, we get interesting results." Adjustment for age, which should be done, has no bearing on whether this post hoc slicing of the data to give good results is proper. This is only OK so long as you're intentionally exploring, and then perhaps doing a follow-up study using those cutpoints in prespecified fashion using fresh data. In other words, if you went back and looked at your data and found the best cutoffs to maximize difference in risk, is it any surprise that you found a big difference in risk? This is why they need to validate these cutpoints in a separate study.

hartror 16y ago
I'm afraid you use the post hoc fallacy incorrectly. The post hoc fallacy is drawing conclusions based on statistically insignificant data sets, normally anecdotes of size 1, at least in every day life. Now assuming this study was done by half way competent statisticians they would have followed the tried and true methods of establishing statistical significance. Sure they could have made mistakes on the way, the entire study could be completely invalidated by a detail or two. But even in that case it isn't a post hoc ergo propter hoc. Your thinking could stem from an affliction brought on by high schools teaching of science. They really only teach one of the scientific methods, that of hypothesis, experimentation, observation and conclusion in that order. However depending on the field of science and the subject at hand that isn't necessarily the order thing are done in. Science isn't a simple linear process like we are lead to believe in high school.
- carbocation 16y ago
  I am speaking in the plain language used by those of us who do science for a living. I'm using the term post hoc in the way that we in science usually do (see http://en.wikipedia.org/wiki/Post-hoc_analysis http://en.wikipedia.org/wiki/Post-hoc_analysis if you're not familiar). I'm not sure how high school science teachers fit into this; sounds like you had a bad experience and I'm sorry if so, but that really has nothing to do with this discussion. There is nothing wrong with "my thinking" but you appear to think so because your understanding is incorrect. Post hoc ergo propter hoc is the fallacy of thinking that, because something comes before another, that first something causes the second. I'm not talking about that at all. Post hoc analysis is something totally different - viz, it's performing non-prespecified analyses. It's more closely coupled to multiple testing than it is to the other fallacy you are referring to. It certainly is not limited to small sample sizes or sample sizes of one, in contrast to what you state. You can do science improperly with samples of any size.
  - _delirium 16y ago
    I could be wrong, but at least in my little corner of science, this particular kind of post-hoc analysis, where you're moving thresholds on a linear scale for reporting purposes, seems not to be viewed with great suspicion, because it doesn't really produce arbitrary degrees of freedom. The worse kind of post-hoc analysis is where you can end up claiming things like "men who watched TV between 5.5 and 9 hours a week [whatever] more than men who watched TV either 1.0-2.0 or 11.5-12.5 hours/week", retroactively discovering a million probably random correlations in your data. Though even there there are ways of reducing the risk of doing so; for example, cross-validation is commonly used to test whether discovered correlations are artifacts of sampling or not, and has some pretty solid statistical theory behind it. But in this case the general hypothesis is pre-chosen and fixed, basically "men who watch TV more hours [whatever] more than men who watch TV fewer hours", and the choice of threshholds is more of a reporting step, pulling out some binned tails at either extreme for emphasis and ease of discussion, while the real correlation is full-curve-to-full-curve.