6 ms·

I am sorry if I am pessimistic but as a person with a genetics background, I am very skeptical when a measure has an error bar (or variance in this case) that i

by patall 6y ago

I am sorry if I am pessimistic but as a person with a genetics background, I am very skeptical when a measure has an error bar (or variance in this case) that includes no-change as a plausible explaination, i.e 25.68%±40.42 And, for my taste, 25.68%±40.42 is really bad style. 26%±40 is the amount of precision I would accept here.

iaw 6y ago
A more informative way for them to express that range would be -15% to 65% where any negative % is no effect.
- mlyle 6y ago
  Where any negative number is a detrimental effect, here. :P
gpm 6y ago
Sorry for the rant, but I hate this obsession with chopping off digits people have. 25.68% ± 40.42 is not the same as 26% ± 40. If I am later trying to make a model to get the best estimate of <stat> by looking at a bunch of other people's measurements I will get a more accurate answer if more digits are contained. When someone presents a state like 26% without the ± it's at least excusable, since it's avoiding making people think that 25.68% was short hand for 26.68% ± (0.005 ± 0.005)%, but when we're including the ± there is no good reason to chop off digits. I do like the comment someone else has about reporting this as a range instead of with a ± sign, i.e. "-14.74 to 66.1 percent".
- cbkeller 6y ago
  This is a pet peeve of mine as well; significant figures can be handy, but fundamentally they originated as a quick and dirty method of keeping track of uncertainty that was quite handy when doing calculations by hand, but is more or less obsolete today any time you have an actual quantitative uncertainty bound. Sure, it's still convenient when writing things out in text to not continue adding digits far beyond the uncertainty of your value, but even so there is nothing "conservative" or in any way accurate about chopping off a bunch of digits (however imprecise) and replacing them with zeros. This is especially clear if we consider that we generally only ever consider sig figs in base 10, but in any computational context that number is definitely not stored in base 10.
- theptip 6y ago
  I think the intuition here is that if your error bars on the measurement are +- 40%, how can your accuracy on the error bars themselves be 4 significant figures, i.e. +-0.1%?
  - Aerroon 6y ago
    >how can your accuracy on the error bars themselves be 4 significant figures They aren't, but that's still the number you got. Chopping off the digits means you're slightly changing the result. It's like Tic Tac and their "0g of sugar" label, despite being 90% sugar.
    - theptip 6y ago
      It’s probably not the number they got though; the STDDEV calculation or whatever error measure you choose will usually give you lots more decimal places. The decision was taken to round to 4 significant figures. If you got 10 decimal places in your calculation, it would obviously be nonsense to give all of them.
    - patall 6y ago
      But the "0g of sugar" in TicTac is something totally different. The difference between 25.68 and 26 is 2% of the total number (while the error of 40 is almost 160%). The difference between 0.01 and 0 is 100%.
      - Erlich_Bachman 6y ago
        Did you just divide by zero?
        WJW 6y ago
        He divided by 0.1, otherwise he would never have gotten to 100%.
      - Aerroon 6y ago
        My point is that it allows these kinds of things to happen. What's the point in making the number less accurate? In probability the difference between 0.999 vs 1.0 is enormous and these kinds of things can happen when you just floor numbers.
- patall 6y ago
  If you really need the precision, you should use the raw data. Why is 25.68 precise enough and not 25.682393. Especially, since it is given in the abstract, and not in some kind of in detail supplement. > If I am later trying to make a model to get the best estimate of <stat> by looking at a bunch of other people's measurements I will get a more accurate answer if more digits are contained. But that is exactly what I doubt: that level of accuracy is not there. You predict a number that has been observed for a sample size of 35. If you now add sample #36, you will almost certainly get a different number. The (round-about, I did not calculate it in any way) two floating point digits are not accurate, they are chance. And a model predicting 25.72 would be no better than one predicting 25.94.
- deleted 6y ago
  [deleted]
xiphias2 6y ago
Generally the longetivity space is full of scams, especially the ones that sell something that doesn't need FDA approval (the last similar thing was a cream company marketed by Peter Diamondis a month ago)
kimi 6y ago
Apart from number of digits (obvious big difference here if it was 40.41 vs 40.42?) I think that the numbers hide the fact that it's more like "I'd bet 5 bucks it's somehow positive" than a definite result you can pin your hat on. On the other hand, I think this study should be the poster child of "please don't use p-values" movement. Everything significant, but floats around zero.

by patall 6y ago

iaw 6y ago

A more informative way for them to express that range would be -15% to 65% where any negative % is no effect.

mlyle 6y ago
Where any negative number is a detrimental effect, here. :P

gpm 6y ago

Sorry for the rant, but I hate this obsession with chopping off digits people have. 25.68% ± 40.42 is not the same as 26% ± 40. If I am later trying to make a model to get the best estimate of <stat> by looking at a bunch of other people's measurements I will get a more accurate answer if more digits are contained. When someone presents a state like 26% without the ± it's at least excusable, since it's avoiding making people think that 25.68% was short hand for 26.68% ± (0.005 ± 0.005)%, but when we're including the ± there is no good reason to chop off digits. I do like the comment someone else has about reporting this as a range instead of with a ± sign, i.e. "-14.74 to 66.1 percent".

cbkeller 6y ago
This is a pet peeve of mine as well; significant figures can be handy, but fundamentally they originated as a quick and dirty method of keeping track of uncertainty that was quite handy when doing calculations by hand, but is more or less obsolete today any time you have an actual quantitative uncertainty bound. Sure, it's still convenient when writing things out in text to not continue adding digits far beyond the uncertainty of your value, but even so there is nothing "conservative" or in any way accurate about chopping off a bunch of digits (however imprecise) and replacing them with zeros. This is especially clear if we consider that we generally only ever consider sig figs in base 10, but in any computational context that number is definitely not stored in base 10.
theptip 6y ago
I think the intuition here is that if your error bars on the measurement are +- 40%, how can your accuracy on the error bars themselves be 4 significant figures, i.e. +-0.1%?
- Aerroon 6y ago
  >how can your accuracy on the error bars themselves be 4 significant figures They aren't, but that's still the number you got. Chopping off the digits means you're slightly changing the result. It's like Tic Tac and their "0g of sugar" label, despite being 90% sugar.
  - theptip 6y ago
    It’s probably not the number they got though; the STDDEV calculation or whatever error measure you choose will usually give you lots more decimal places. The decision was taken to round to 4 significant figures. If you got 10 decimal places in your calculation, it would obviously be nonsense to give all of them.
  - patall 6y ago
    But the "0g of sugar" in TicTac is something totally different. The difference between 25.68 and 26 is 2% of the total number (while the error of 40 is almost 160%). The difference between 0.01 and 0 is 100%.
    - Erlich_Bachman 6y ago
      Did you just divide by zero?
      - WJW 6y ago
        He divided by 0.1, otherwise he would never have gotten to 100%.
    - Aerroon 6y ago
      My point is that it allows these kinds of things to happen. What's the point in making the number less accurate? In probability the difference between 0.999 vs 1.0 is enormous and these kinds of things can happen when you just floor numbers.
patall 6y ago
If you really need the precision, you should use the raw data. Why is 25.68 precise enough and not 25.682393. Especially, since it is given in the abstract, and not in some kind of in detail supplement. > If I am later trying to make a model to get the best estimate of <stat> by looking at a bunch of other people's measurements I will get a more accurate answer if more digits are contained. But that is exactly what I doubt: that level of accuracy is not there. You predict a number that has been observed for a sample size of 35. If you now add sample #36, you will almost certainly get a different number. The (round-about, I did not calculate it in any way) two floating point digits are not accurate, they are chance. And a model predicting 25.72 would be no better than one predicting 25.94.
deleted 6y ago
[deleted]

xiphias2 6y ago

Generally the longetivity space is full of scams, especially the ones that sell something that doesn't need FDA approval (the last similar thing was a cream company marketed by Peter Diamondis a month ago)

kimi 6y ago

Apart from number of digits (obvious big difference here if it was 40.41 vs 40.42?) I think that the numbers hide the fact that it's more like "I'd bet 5 bucks it's somehow positive" than a definite result you can pin your hat on. On the other hand, I think this study should be the poster child of "please don't use p-values" movement. Everything significant, but floats around zero.