Date: Thu, 23 Sep 2004 12:38:32 +1000
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Paul Dickson <email@example.com>
Subject: Re: Odd SPSS Results - Zero Coefficient
You stated the following: "You reject the null hypothesis that the true value is zero
whenever the confidence interval does not include the zero value".
That is correct as far as I am concerned. But, I am wondering based on the thread of
this discussion whether you meant in cases where the t-test (p-value) of the beta
weight is significant, but your confidence intervals overlap with zero. If they are
different (CI contains a zero value within the range) but (the P-value is significant) I
myself would defer to the t-test, because while both CI and P-values assume
probability sampling because they are inferential statistics (most data evaluated is not
drawn from individuals with an equal likelihood of being selected) I would think in a
case like this that CI's are less robust to violations of this assumption. Correct me if I
am wrong here. Would this also not make the p-value a more robust criterion for
decision making. What do others do if the confidence interval around the beta weight
includes 0, but the p-value for the same beta-weight is significant. Is this possible,
since the t-tests in spss are based on a similar decision criteria ideal to the CI where it
is assumed that each coefficient = 0 (null hypothesis) when the t-test is run on the
> Hector Maletta <firstname.lastname@example.org> wrote:
> In fact I have reread the thread and think I misunderstood the question
> consequently confused the matter more than clarifying it, and apologize
> The 95% confidence interval of an estimate of a regression coefficient
> based on 1.96 times its standard error. If that confidence interval
> the zero value, you cannot reject the null hypothesis that the true
> value is
> zero. Thus, if your estimate is (nearly) zero and nonetheless you
> obtain a
> very low p value, it means the probability of the true value being zero
> very low. You should rescale the results and will probably see that the
> estimate is not exactly zero.
> However, I have my suspicions about part of Richard Ristow's comments,
> > At 02:41 PM 9/22/2004, Hector Maletta wrote:
> > >If there is a population value for the coefficient, and you
> > estimate it
> > >obtaining a value of zero or any other, the significance
> > tells you that
> > >the true value is 95% likely to be within two standard
> > errors from your
> > >estimate.
> > Is this really true? I thought that the population value
> > would fall within two standard-errors-of-estimate of the
> > estimated value, in any case.
> "In any case" seems too much for me. You have a probability that it
> within a certain interval (usually 2SE corresponding to a 95%
> and a probability (usually 5%) that it lies outside that interval. If
> probability is larger than 95% the interval is wider, say 3SE or
> and it is narrower if the probability is less than 95%. You reject the
> hypothesis that the true value is zero whenever the confidence interval
> not include the zero value. Suppose the estimate for the coefficient is
> 0.25, and the SE is 0.1. A 95% interval means an interval of 2 SE, i.e.
> approximately 0.2 to each side of your estimate, ranging from 0.05 to
> Since this interval does not include the zero, you have 95% confidence
> the null hypothesis (which says the true value is zero) is false
> there is a 5% probability that it is true).
> Am I so terribly wrong in this?