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Re: unemployment stats

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On October 5, Rich Ulrich wrote:
>>>> Given a population of unemployed persons, i.e. names 
>>>> and phone numbers. You wish to construct a histogram 
>>>> of # of persons vs. time (# of days) out of work. 
>>>> Stats 101, right? 
>>>> Call some random subset of the list, ask them: when 
>>>> were you laid off? Assuming the sample is unbiased, 
>>>> it will satisfy the conditions. 
>>>> No, this method is flawed. Because the person out of 
>>>> work a long time, has a greater chance of receiving 
>>>> multiple calls (or at least one call) than one who is 
>>>> shortly re-employed. This biases the sample, skews 
>>>> the numbers on the long side. 
> 
>>> Well, the number represents what it represents. 
>>> It is only a mis-report of you mis-report it. 
> 
>>>> Therefore, officially published statistics are unreliable. 
> 
>>> I think you mean "invalid". And you are wrong, mainly. 
>>> Technically, in statistics, we have both "reliability" and 
>>> "validity"...  good validity says that it measures 
>>> what it purports to measure. You should complain about 
>>> validity: the statistics imply something untrue. 
> 
>>Given the goal of the study, is the objection mentioned above, justified? 
>>i.e. is the methodology flawed?
>
> My position is that you can collect and report information for 
> any numbers that might be interesting. 
> The initial problem is, "Where do these data come from?" - That 
> might put hard limits on what you can infer. 
> You are jumping ahead to "bad inference." Showing a 
> histogram of a cross-section of a stated population (sample) 
> is not "drawing an inference." 
> Assuming a simplified, instantaneous cross-sectional sample from 
> that population, you might use your observations above, 
> about the implicit weighting, to compute a weighted mean -- 
> Each person would be weighted by their TIME (as the 
> "probability of being sampled") and you compute that weighted 
> mean... as an estimate of ... hmm. 

The goal isn't to estimate the chance a person might receive a call.
The goal is to estimate the distribution of population vs. time unemployed, 
given a histogram of samples of the unemployed.  Then, perhaps, one might 
predict, probabilistically, how much time a newly unemployed will require to 
find new work.

Intuitively, the distribution should  match the sample histogram.  That's the 
desired inference.  Very simple.

Given all that, review the objection mentioned above: those longer unemployed, 
will have a greater chance of getting a call.  Therefore, the methodology is flawed; 
the sample isn't unbiased.

I have an ulterior motive for posting this - 

-- 
Rich 

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unemployment stats RichD <r_delaney2001@yahoo.com> - 2021-10-03 16:35 -0700
  Re: unemployment stats Rich Ulrich <rich.ulrich@comcast.net> - 2021-10-04 14:35 -0400
    Re: unemployment stats RichD <r_delaney2001@yahoo.com> - 2021-10-04 19:37 -0700
      Re: unemployment stats Rich Ulrich <rich.ulrich@comcast.net> - 2021-10-05 20:40 -0400
        Re: unemployment stats RichD <r_delaney2001@yahoo.com> - 2021-10-08 12:04 -0700
          Re: unemployment stats RichD <r_delaney2001@yahoo.com> - 2021-10-08 12:10 -0700
          Re: unemployment stats Rich Ulrich <rich.ulrich@comcast.net> - 2021-10-09 19:10 -0400
            Re: unemployment stats RichD <r_delaney2001@yahoo.com> - 2021-10-23 17:18 -0700
              Re: unemployment stats Rich Ulrich <rich.ulrich@comcast.net> - 2021-10-24 13:39 -0400

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