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"...If the question is about how many pulls is it going to take to
start my lawn mower, that's completely different issue than how much
data do I have to test to make sure I have a really high probability
of making money on the next 100 trades..."
Only if you don't know how your lawn mower works.:) If you know that
it won't start without any gas in the tank you know for a 100% fact
that it won't start, no matter how many pulls you make. If you took
the blade off to sharpen it but didn't put the blade back on, it also
won't start no matter how many pulls. If the "auto-stop" safety
lever isn't engaged, it also won't start.
On the other hand, if you understand how the machine works and it's
in good mechanical condition there's an excellent chance it will
start with one or two pulls every time you're ready to mow, and you
don't need to do a thousand tests to make sure.
Same principle applies to any trading method. If you only use it
because the back-testing results looked good without understanding
why the method works, it could stop working on your very next trade
and every one you take after that, even though it tested well all the
way up until yesterday and you wouldn't have any idea why it suddenly
failed. In fact, that very situation is the reason for the cliche
investing-joke, "It was doing great all the way up to the point that
I put money in it, then it went down.":)
Luck,
Sebastian
--- In equismetastock@xxxxxxxxxxxxxxx, superfragalist <no_reply@xxxx>
wrote:
> In my last post I talked about some of the problems with statistical
> inference. I probably should have mentioned statistical significance
> versus statistically right.
>
> The central limit theorm and the number 30 trades was mentioned in
> response to someone asking how many trades it takes to be
> statistically sound.
>
> I don't know what the definition of statistically sound is but the
> answer that MG gave refers to statistically significant.
> Unfortunately, something being statistically significant does not
> address the question of dependibility.
>
> Statistically significant is a nummerical threshold that relates to
> sample size versus the size of the universe of data, or the
population.
>
> At the threshold of statistical significance, I would be very
> reluctant to place my money on any defined outcomes.
>
> In regard to statistics, the definition of how much is needed to
> produce a good enough answer is based on your defintion of how good
is
> good enough. That varies a lot from person to person and situation
to
> situation.
>
> If the question is about how many pulls is it going to take to start
> my lawn mower, that's completely different issue than how much data
do
> I have to test to make sure I have a really high probability of
making
> money on the next 100 trades.
>
> If there's a 2% chance you won't survive a surgical procedure
that's a
> huge potential negative outcome. If there's only a 2% chance you'll
> lose your money, where's the casino!
>
> That's wrong headed thinking. Casino's make an enormous amount of
> money from people that think a 2% or less chance of losing is good
> odds. And a lot of people die from procedures that 98% survive.
>
> The central limit theorm doesn't understand nor care about the
> difference between how many pulls it takes to start my lawn mower or
> the percentage of people who die from a surgery.
>
> If the central limit theorm says that a sample size of 30 is
> statistically significant and my doctor concludes that based on
having
> done the surgery 30 times, I only have a 2% chance of dying, I might
> feel somewhat reluctant to embrace the central limit theorm in
regards
> to trusting it with my life. I may want to tell my doctor to call me
> when there's been 30,000 of these surgeries and give me my odds.
>
> I suppose it all depends on how certain you need to be about the
> probability of an outcome before you're willing to bet on it.
>
> Like I said, the mathematics of probability can be a cruel teacher.
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