From: lambert@spectrum.cs.unsw.oz.au (Tim Lambert)
Subject: Re: A Coffee-House Conversation with an Imaginary Nadja
Organization: CS&E, Uni of NSW, Australia
	<LAMBERT.92Dec18023741@nankeen.spectrum.cs.unsw.oz.au>
	<1gqp17INNlps@roche.csl.sri.com>
Date: Mon, 21 Dec 1992 15:43:44 GMT

This thread should probably go to email.  However, since Peter has
seen fit to publicly accuse me of dishonesty, I will respond
publicly.

>>>>> On 17 Dec 1992 20:47:35 GMT, boucher@csl.sri.com (Peter K. Boucher) said:

> In this thread, lambert has consistently misrepresented the facts regarding
> both the data he supplies, and my answers to his arguments.  He has done
> this to such an extent as to leave no doubt that he is either a) completely
> clueless, or, more likely, b) dishonest.

> Let's examine the case:

> TL = Tim Lambert
> PB = Peter Boucher

>TL>Kennesaw Burglaries 1976-1986
>TL>76 77 78 79 80 81 82 83 48 85 86
>TL>41 21 22 35 35 54 35 35 29 32 70

>TL> Clearly the Kennesaw ordinance had no effect.

The two quotes above come from two different postings.  Here is the
second one, back in it's original context:


] The raw data is presented as graphs showing burglaries in Morton Grove
] and Kennesaw for the period 1976-86.  The burglary rate in Kennesaw
] appears stable, while the burglary rate in Morton Grove is noticeably
] lower.  The paper's authors did an interrupted time series analysis on
] the data:  the change associated with the Kennesaw ordinance was an
] insignificant increase of 0.2 burglaries/month, the
] change associated with the Morton Grove ordinance was a
] statistically significant decrease of 4.5 burglaries/month,

] Clearly the Kennesaw ordinance had no effect.  It seems unlikely that
] the Morton Grove ordinance caused the reduction (unless there a
] significant number of burglars who burgle primarily to get guns).  The
] authors also considered the effect of the Evanston gun ban on
] burglaries and found a not quite significant (p=.13) decrease.

] The data does not support the theory that gun ownership deters
] burglaries.


> Let's look at a graph of the data:

> burglary
> rate
>                          |          /--
>   70 |                   |          |XX
>      |                   |          |XX
>   60 |               /--\|          |XX
>      |               |XX |          |XX
>   50 |               |XX |          |XX
>      | --\           |XX |          |XX
>   40 | XX|     /----/ XX |----\     |XX
>      | XX|     |XX XX XX |X XX ----/ XX
>   30 | XX|     |XX XX XX |X XX XX XX XX
>      | XX\----/ XX XX XX |X XX XX XX XX
>   20 | XX XX XX XX XX XX |X XX XX XX XX
>      --------------------|---------------------
>        76 77 78 79 80 81 82 83 84 85 86   Year
>                          |
>                          |
>                The year the law passed.

In his classic book "How to Lie With Statistics" Darrell Huff notes
that if you want to make changes to a data set look more dramatic, a
useful technique is to leave off the bottom part of the graph and
start the scale at something other than 0.

> Mr. Lambert supplies no data regarding years before 76 nor after 86,
> so both those years are seen more-or-less in isolation, and thus are
> suspect, regarding their relevance.  We don't know what might have
> caused an increase in 76 or 86.  We don't know what might have caused
> a decrease in 77.  We also don't know what caused a decrease in 82,
> and cannot rule out the possibility that it was caused by the law.

In an earlier posting, Peter stated that it was apparent that "the law
had a marked effect".  In order for this to be apparent, it is
necessary to ignore the years 76 and 86.  The paragraph above is a
rationalisation for this.

If we exclude 76 and 86 because they are the end years of the data
set, we are left with data for 77-85.  Now 77 and 85 are the end years
and also must be excluded.  Repeat as many time as necessary to get
the data to support yout theory :-)

> However, anyone with the common sense of a "potatoe" can see that the
> rate was steadily increasing from 77-81, took a sharp dip the year the
> law was passed (82), and stayed down from 82-85, before rising again
> in 86.

Apart from three peaks in 76,81 and 86 the burglary rate stayed in the
range  28+/-7.  (If burglaries are Poisson distributed, that's +/- 1.3
standard deviations.)  The peak in 81 occured before the law so could not
have been caused by it.  Nor is it likely that the 81 peak caused the
law, since the stated reason was to protest the Morton Grove gun ban.
The highest peak occurs in 86, after the gun law, but a delayed effect
seems unlikely to me.

> Now this does not show conclusively that the law had any effect, but
> it does show conclusively that Lambert's claim that these numbers show
> that "Clearly the Kennesaw ordinance had no effect" is baseless.

If you wish to argue that the law might have caused some small
increase or decrease in burglaries that could not be detected because
of random fluctuations in the rate, I would not argue with you, but
you said "the law had a marked effect".

>TL>Fortunately we can distinguish the effect of these factors from the
>TL>possible effect of the gun law.  The gun law is supposed to have had
>TL>an abrupt effect, while the other factors would operate gradually over
>TL>the whole period.  If some other factor (such as increasing
>TL>population) caused the number of burglaries to double over the ten
>TL>years, cancelling out a 50% drop caused by the law, then the graph
>TL>would look like this:
>TL>             *****
>TL>        *****     *
>TL>    ****
>TL>****               *         *****
>TL>                         ****
>TL>                    *****

> If you smooth out the graph I've drawn, its general shape is represented
> by the graph Lambert gives above.  According to Lambert, this indicates
> that "some other factor (such as increasing population) caused the number
> of burglaries to double over the ten years, canceling out" a drop caused
> by the law.  However, paradoxically, Lambert uses this same data as
> evidence of the opposite. ;^)

Smoothing it out gives something like this (2 year moving average):
         *

    **
   *  *
* *    **
 *
or this (3 year moving average)
   ***  *
      *
* *    *
 *

Even if the smoothed data fitted the sawtooth pattern, the conclusion
you draw does not follow.  (If A implies B, and B is true, it does not
follow that A is true.)

>PB> What is this, a joke?  The graph you've drawn represents a
>PB> generalization of the shape of the graph made of the numbers you
>PB> supply.  Are you hoping people won't look at any but the first and
>PB> last of the series?  The numbers are increasing from 77-81, take a
>PB> sudden dip in 82 (the year the law passed), stay down from 82-85
>PB> before rising again in 86.  Increases followed by a dip followed by
>PB> more increases.

>TL> You seem to have left out a year (76) in your description of the data.
>TL> I can also leave out a year and get an equally valid description.
>TL>
>TL> 41 21 22 35 | 35 35 35 29 32 70
>TL>
>TL> Let's see, a bit of a dip, then flat, then another dip, and then an
>TL> increase.

> Even this would not support Lambert's claim that "Clearly the Kennesaw
> ordinance had no effect."  Lambert is assuming that the rate would have
> been flat from 79-83 without the law.  Where is his evidence for this?

Note that this argument can be used regardless of what the actual
numbers are.  Even if the data looked like this
     ************
     *
     *
******
     |
     Law

Peter could ask evidence (nature unspecified) that proved that the
rate would nor have increased even more, had it not been for the law.

> (This is for Lambert:  Lambert, please read this line three times before
>                        replying.  I am not claiming that the law had any
>                        effect, but showing that your claims that it did
>                        not are totally unsupported by the data you cite.)

My apologies.  When you stated

> It appears to me that, according to your own figures, the law had a marked 
> effect.  

I assumed that you were claiming that the law had a marked effect.

Tim
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