The size of the confidence interval is determined by the range it specifies 
(i.e., a 95% CI is calculated using Z=1.645 or 1.96, one or two tailed).  The 
chart should indicate that they depict a 95% CI or 99% CI or whatever it is.  

There are a couple of common problems that I believe should be taught to 
students, particularly graduate students but also advanced undergraduate 
methods students.  
1) The confusion between interpreting CI and standard error bars.  What 
constitutes a difference between two means with 95% CIs?  Many believe that the 
CI must not overlap, which is not actually the case but does result in a very 
conservative approach, and when comparing multiple means probably not a bad 
idea.  I was taught, but now question, the idea that so long as the mean of 
each group is outside the CI of the "other" group there was a difference at the 
.05 level (assuming 95% CIs).  The idea is that the CI around mean A represents 
the region containing the actual mean of the population with 95% certainty.  
Thus a mean for group B that falls outside the CI is only likely to represent a 
sample from population A 5% of the time (ignoring directionality for the time 
being).  BUT... you must also consider the CI around group B, because while the 
mean for B might not be from population A, you should also check that the mean 
from A is not from population B and thus you must look at BOTH confidence 
intervals.  This is only a heuristic and not an absolute, I always tell me 
students that to draw an inference that will be included in the discussion they 
must perform an inferential test (the CI are for the readers not for the 
researchers).  Standard error bars however require no overlap of the ranges 
centered on each mean.  See Belia et. al (2005) for an interesting study of 
published authors and their understanding of CI and standard error bars.  

Belia, S., Fidler, F., Williams, J., & Cumming, G.  (2005).  Researchers 
Misunderstand Confidence Intervals and Standard Error Bars.  Psychological 
Methods, 10, 389-396.  APA  DOI: 10.1037/1082-989X.10.4.389

2) Some programs automatically compute CI based on the entire sample and not 
for each individual mean (Excel uses only the deviation of the means).  The CI 
for each mean must be computed specifically for that value.  CI's can thus vary 
widely from one mean to another and if all of them look to be the same size 
around 5 different means there is probably something going on (unless you have 
a pretty good sized sample - of course at very large sample sizes the CIs are 
pretty small).



Doug  




Doug Peterson, PhD
Associate Professor of Psychology
The University of South Dakota
Vermillion SD 57069
605.677.5223
________________________________________
From: Jim Clark [[email protected]]
Sent: Thursday, April 19, 2012 9:12 PM
To: Teaching in the Psychological Sciences (TIPS)
Subject: RE: [tips] confidence intervals

Hi

But isn't there a p involved in the CI (or rather 1 - p)?  I'm not sure how one 
interprets a CI without some notion of p or its inverse.  For example, why do 
we choose z = 1.645 or 1.96 or 2.333 or whatever to construct the CI?

Take care
Jim


James M. Clark
Professor of Psychology
204-786-9757
204-774-4134 Fax
[email protected]

>>> "Wuensch, Karl L" <[email protected]> 19-Apr-12 9:04 PM >>>
I have always introduced estimation, point and interval, prior to Statistical 
Hypothesis Inference Testing.  After introducing hypothesis testing, I note 
that once one has a confidence interval, e can generally decide whether or not 
a hypothesis fits well with the observed data or not, no pee necessary.
Cheers,
[Description: Karl L. Wuensch]<http://core.ecu.edu/psyc/wuenschk/klw.htm>
From: Marte Fallshore [mailto:[email protected]]
Sent: Tuesday, April 17, 2012 4:55 PM
To: Teaching in the Psychological Sciences (TIPS)
Subject: [tips] confidence intervals

I was at the Rocky Mountain Psychological Association meeting last weekend, and 
there was a talk on confidence intervals. It got me thinking about teaching 
about confidence intervals before I get to hypothesis testing and then 
integrating it with each hypothesis test we do.

Has anyone out there done that? How did it go? Have you found a book that may 
be does something like that? Thanks,

Marte


************************************************
Marte Fallshore
Department of Psychology
Central Washington Univ.
400 E University Way
Ellensburg, WA 98926-7575

509/963-3670
509/963-2307 (fax)
Room 462, Psychology Building

Everyone is entitled to their own opinions, but they are not entitled to their 
own facts. ~Daniel Patrick Moynihan

When I give food to the poor, they call me a saint.
When I ask why the poor have no food, they call me a communist.
        ~Dom Heider Camara

I teach for free; they pay me to grade. (anon)
************************************************


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