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 Lecture on Estimation

January 9, 2019
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0:00 what is estimation well there's no way

0:03 you can sample the entire population the

0:06 only thing you can do is get a sample of

0:08 it from that sample hopefully the sample

0:11 is large enough because we've decided

0:12 what our outcome is going to be we can

0:14 generate an estimate of how much power

0:17 or how much sample size we're going to

0:19 need to detect that difference go out

0:21 and sample it generate parameters from

0:25 that what's a parameter parameter of a

0:34 model mean standard error those are

0:44 parameters that come and play a role in

0:46 defining the model and then an

0:50 estimation once you get the model if

0:52 it's right which is a big if sometimes

0:55 but large enough sample size do you guys

0:59 remember the central limit theorem from

1:01 statistics central limit theorem what is

1:06 that basically the central limit theorem

1:10 says no matter what you start with if

1:12 you get a large enough sample size with

1:14 repeated sampling you're gonna get a

1:18 normal distribution ultimately

1:20 everything will collapse to a normal

1:23 bell shaped curve with enough sample

1:25 your sample may not be large enough to

1:28 do that so you might have to use an

1:31 alternate distribution like chi-square

1:34 distribution or like an F distribution

1:36 or a Pareto distribution there are many

1:40 different types of distributions a T

1:42 distribution ultimately with enough

1:44 sample size in the correct model they

1:46 will converge to being a normal the

1:49 normal allows you to make

1:50 generalizations about the larger

1:52 population right larger population for

1:55 more or less is going to be normal so if

1:58 your sample has the same distribution as

2:00 the population your sample is a good

2:02 sample and allows you to make estimation

2:04 on that population now hence the crystal

2:07 ball kind of feeling statistics allows

2:09 you to get a feeling from what the gym

2:11 population would have
    
  
Disclaimer:
              Opinions or points of view expressed in these recordings represent those of the speakers and do not necessarily represent the official position or policies of the U.S. Department of Justice. Any commercial products and manufacturers discussed in these recordings are presented for informational purposes only and do not constitute product approval or endorsement by the U.S. Department of Justice.
  
Date Published: January 9, 2019
    
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