Well, I'd guess you'd say the glass is half empty then.  In my research,
accounting for any individual differences in language acquisition is hard
enough, so I'll take every 1% variance accounted for I can get.  I try to
convey this attitude when I teach effect sizes.   My take on it is that when
one starts adding all the 1%s together, pretty soon one get some pretty
sweet variance accounted for.  And in any case, science doesn't progress by
leaps and bounds, it progresses incrementally, over time, when all the
little 1%s add up. 

I might add that there is a huge push among funding agencies to fund
projects with large enough sample sizes to detect small effects; so I guess
it's not surprising that there are large N studies and small effect sizes.
But I'm not sure I would regard them as automatically lacking clinical
significance or meaningfulness; I'm too committed to science as a
transgenerational institution to give up on the little bits of variance
accounted for that's sprinkled throughout it.

Wally Dixon




On 8/16/04 5:36 PM, "Paul Okami" <[EMAIL PROTECTED]> wrote:

> I suspect that the opposite is far more often the case:  Statistically
> significant results due only to the fact that large samples were used
> represent lack of clinical significance or meaningful group differences.
> 
> Paul
> ----- Original Message -----
> From: "Wallace Dixon" <[EMAIL PROTECTED]>
> To: "Teaching in the Psychological Sciences" <[EMAIL PROTECTED]>
> Sent: Monday, August 16, 2004 5:17 PM
> Subject: Re: teaching effect size
> 
> 
>> Maybe I was too sloppy in my usage.  But what I *meant* to say still
>> applies. The larger the sample, the smaller the effect size that can be
>> detected to be statistically significant.  So with extremely large
> samples,
>> extremely small effect sizes can be detected to be statistically
>> significant.  But it is not automatically true that small effect sizes due
>> to large samples are not meaningful, they can be very meaningful.  So,
>> people may laugh at you if you say "Hey, I accounted for 1% of the
>> variance!"  But pragmatically, accounting for 1% of the variance may
> benefit
>> millions of peoples lives in America alone.
>> 
>> wedj
>> 
>> On 8/16/04 4:38 PM, "Paul Okami" <[EMAIL PROTECTED]> wrote:
>> 
>>> Small effect sizes in large populations are not the same as statistical
>>> significance due to large samples.  This is what I was referring to.
>>> 
>>> Paul Okami
>>> ----- Original Message -----
>>> From: "Wallace Dixon" <[EMAIL PROTECTED]>
>>> To: "Teaching in the Psychological Sciences" <[EMAIL PROTECTED]>
>>> Sent: Monday, August 16, 2004 12:56 PM
>>> Subject: Re: teaching effect size
>>> 
>>> 
>>>> But you have to be careful about this.  As noted by Rosenthal, small
>>> effects
>>>> sizes in large populations do not mean unimportant.  Effect sizes as
> small
>>>> as 1% can still translate into an influence on millions of people in
> the
>>> US,
>>>> as was the case in the aspirin study and the decreased rate of second
>>> heart
>>>> attacks.
>>>> 
>>>> Wally Dixon
>>>> 
>>>> 
>>>> 
>>>> 
>>>> On 8/16/04 12:25 PM, "Paul Okami" <[EMAIL PROTECTED]> wrote:
>>>> 
>>>>> I usually give an example of results that are statistically
> significant
>>>>> because of large samples, but have little meaning in the real world.
> In
>>>>> other words, they're "real" but "not important."
>>>>> 
>>>>> That makes it easy to talk about a statistic that reflects magnitude
> of
>>>>> group differences.  It's really very intuitive.  (I don't discuss the
>>> math
>>>>> involved).
>>>>> 
>>>>> Paul Okami
>>>>> ----- Original Message -----
>>>>> From: "Hatcher, Joe" <[EMAIL PROTECTED]>
>>>>> To: "Teaching in the Psychological Sciences"
> <[EMAIL PROTECTED]>
>>>>> Sent: Monday, August 16, 2004 12:18 PM
>>>>> Subject: teaching effect size
>>>>> 
>>>>> 
>>>>>> Hi Tipsters,
>>>>>> 
>>>>>> I apologize for the cross-posting.
>>>>>> 
>>>>>> I teach the first semester of our two-semester Research Design and
>>>>>> Statistics class, my part of which goes through t-tests and requires
> a
>>>>>> completed project.  When I first started teaching the course, "effect
>>>>> size"
>>>>>> wasn't one of the topics covered.  In trying to update the class, I
>>> would
>>>>>> like to teach this concept, but I'm having some trouble, as it
> doesn't
>>>>> come
>>>>>> as naturally to me as do some of the other topics, and most of the
>>>>>> explanations of effect size seem very complicated.
>>>>>>         Can someone point me to a method to teach effect size that is
>>>>> easily
>>>>>> understood by undergraduates?
>>>>>> 
>>>>>> Joe Hatcher
>>>>>> Ripon College
>>>>>> Ripon, WI
>>>>>> [EMAIL PROTECTED]
>>>>>> 
>>>>>> 
>>>>>> 
>>>>>> Joe W. Hatcher, Jr., Ph.D.
>>>>>> Psychology
>>>>>> Ripon College
>>>>>> Ripon, WI 54971
>>>>>> USA
>>>>>> [EMAIL PROTECTED]
>>>>>> 
>>>>>> ---
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>>>>> 
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>>>>> 
>>>> 
>>>> 
>> 
>>> -------------------------------------------------------------------------
> -
>>> --
>>>> Wallace E. Dixon, Jr.              |
>>>> Chair and Associate Professor      |     If children grew up according
> to
>>>>   of Psychology                    |     early indications, we should
> have
>>>> Department of Psychology           |     nothing but geniuses.
>>>> East Tennessee State University    |       -Johann Wolfgang von Goethe
>>>> Johnson City, TN 36714             |
>>>> (423) 439-6656                     |
>> 
>>> -------------------------------------------------------------------------
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>>> --
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>> 
>> --------------------------------------------------------------------------
> --
>> Wallace E. Dixon, Jr.              |
>> Chair and Associate Professor      |     "When I turned two I was really
>>   of Psychology                    |     anxious because I'd doubled my
>> Department of Psychology           |     age in a year."
>> East Tennessee State University    |       -Steven Wright
>> Johnson City, TN 36714             |
>> (423) 439-6656                     |
>> --------------------------------------------------------------------------
> --
>> 
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