| From: | Claudio Freire <klaussfreire(at)gmail(dot)com> |
|---|---|
| To: | Greg Stark <stark(at)mit(dot)edu> |
| Cc: | Albe Laurenz <laurenz(dot)albe(at)wien(dot)gv(dot)at>, Mark Kirkwood <mark(dot)kirkwood(at)catalyst(dot)net(dot)nz>, PostgreSQL-development <pgsql-hackers(at)postgresql(dot)org>, Robert Haas <robertmhaas(at)gmail(dot)com>, Josh Berkus <josh(at)agliodbs(dot)com> |
| Subject: | Re: ANALYZE sampling is too good |
| Date: | 2013-12-10 14:32:14 |
| Message-ID: | CAGTBQpbnhsc7h4fCHBG63kSYt3-DmyeTZ-QjGf30dN-xacrK4Q@mail.gmail.com |
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| Lists: | pgsql-hackers |
On Tue, Dec 10, 2013 at 11:02 AM, Greg Stark <stark(at)mit(dot)edu> wrote:
>
> On 10 Dec 2013 08:28, "Albe Laurenz" <laurenz(dot)albe(at)wien(dot)gv(dot)at> wrote:
>>
>>
>> Doesn't all that assume a normally distributed random variable?
>
> I don't think so because of the law of large numbers. If you have a large
> population and sample it the sample behaves like a normal distribution when
> if the distribution of the population isn't.
No, the large population says that if you have an AVERAGE of many
samples of a random variable, the random variable that is the AVERAGE
behaves like a normal.
The variable itself doesn't.
And for n_distinct, you need to know the variable itself.
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