Keywords: statistical significance; electronic publishing; publication bias; null hypothesis;
meta analysis; editorial decision making; hypothesis testing

David M. Lane
Department of Psychology
Rice University
Houston, TX
77005 USA

Miguel A. Quinones
Department of Psychology
Rice University
Houston, TX
77005 USA

ABSTRACT: The debate over the use of statistical significance testing in the social
sciences has heated up in recent years. This paper presents a brief review of the common
criticisms of significance testing and argues that, as long as journal editors must
choose among a number of manuscripts, significance testing provides useful
information for making these choices. Electronic publishing is presented as a way
of resolving the current impasse in this debate.


1. In a recent article, Schmidt (1996) portrays two visions of statistics and its
role in psychological research. In the first, statistical significance testing is the
primary means of data analysis. Although researchers are happily using the
significance tests they learned in graduate school, these researchers have an
inadequate understanding of the underlying logic of statistical inference. As a
result, they use significance tests to answer questions that significance tests, in
principle, cannot answer. This frequently leads to such serious misinterpretations
of results that the accumulation of scientific knowledge is severely impede d.
In the second vision, the shackles of significance testing have been thrown off
and researchers rely primarily on interval estimation to analyze their results. Data
from well-designed experiments are published without regard to significance
testing and meta-analysis is used to combine the results across studies to reach
valid conclusions.

2. Schmidt argues that significance testing has nothing valid to contribute to the
analysis of data and that it should neither be taught in graduate school statistics
courses nor included in published reports of scientific works. Although we are
sympathetic to many of Schmidt's points, and agree with him that his second
vision is one to be strived for, we believe Schmidt has overlooked an important
characteristic of significance testing: significant results are more conclusive than
nonsignificant results.

3. We begin this paper by summarizing the arguments of Schmidt and others
(e.g., Cohen, 1994) against the use of significance tests. We then present the
argument that significant results are more conclusive than nonsignificant results.
This leads us to conclude that given the premium on journal space, editorial
decisions will be and should be heavily influenced by the results of significance
tests. This makes it very difficult to move toward Schmidt's second vision of the
role of statistics. The proposed solution lies in a new model for publishing and
editorial decision making based on electronic publishing on the World Wide Web.


4. Numerous authors have pointed out errors that psychologists and other
researchers have made interpreting and applying significance tests (see Chow,
1996, for a review). Among these errors are (1) accepting the null hypothe sis for
nonsignificant results, (2) interpreting the probability value as the probability the
null hypothesis is false, (3) confusing statistical significance and effect size, and
(4) interpreting the probability value as the probability of obtaining a significant
outcome in a subsequent finding. Schmidt argued convincingly that the first of
these errors, accepting the null hypothesis, has been the most detrimental to the
advance of knowledge. As an example, Schmidt described how the failure of some
studies to find significant correlations between employment tests and job
performance led to numerous investigations of a phenomenon that is best explained
as sampling error.

5. The crux of criticisms of significance testing is not that significance testing is
misused, for that would only indicate that it should be used correctly. Instead, the
argument is that significance testing has nothing of value to offer and that
researchers use it because they mistakenly believe that significance testing
provides information that, in fact, it does not. For example, most researchers
would like their statistical analysis to provide the probability that the null
hypothesis is false. Instead, significance testing gives only the probability of the
outcome (or a more extreme outcome) given that the null hypothesis is true.
Critics of significance testing claim that most users of significance testing
believe they have an objective way to determine whether or not a null hypothesis is
true. However, in most if not all realistic experimental situations, the prior
probability of the null hypotheses being true is essentially zero. To illustrate,
suppose an experimenter were interested in comparing two methods for teaching
subjects how to use a piece of computer software. It is not conceivable that the
population difference between the two methods could be exactly zero. If the
(population) mean time to perform a task after being trained with Method A is
3.6242541 minutes, is it conceivable that 3.6242541 minutes is also the mean time
to perform after being trained with Method B? The argument, therefore, is that this
whole to do about rejecting the null hypothesis is much ado about nothing s ince the
null hypothesis is virtually always known to be false in the first place (cf. Cohen,


6. Despite these and other criticisms of significance testing, significance tests do
make one important contribution: they indicate whether or not a set of
experimental data is conclusive. Consider a "crucial" experiment in which
competing theories make opposite predictions: Theory 1 predicts subjects in
Condition A should outperform subjects in Condition B whereas Theory 2 predicts
the opposite. Assume, for the sake of argument, that it is implausible that the two
conditions result in exactly the same (population) level of performance. That
leaves two possibilities: (1) Condition A > Condition B and (2) Condition A <
Condition B.

7. Assume that an experimenter does a significance test and finds that the
difference is statistically significant at the .01 level and that the mean for Condition
A is greater than the mean for condition B. This finding allows the researcher to
make a statement about the 99% confidence interval on the difference between
population means. It is well known that if a statistic (the difference between
sample means in this case) is significantly different from a hypothesized value
(zero in this case) then the confidence interval associated with the significance test
(99% confidence interval for .01 level) will not contain the hypothesized
parameter. For this example, the significant outcome means that the 99%
confidence interval on mean A -mean B does not contain zero. Instead, all values in the
interval will be greater than zero. Thus all "plausible" values of mean A - mean B will be
positive and the experimenter will be justified in concluding that mean A > mean B and
therefore that Theory 1 is supported.

8. If the result had not been significant, then values on both sides of zero would
have been included in the 99% confidence interval on mean A -mean B. This means that
both theoretical outcomes are still plausible: mean A could be greater than mean B but mean
B could be greater than mean A.

9. Although theories rarely live or die based on the result of a single study, a
significant result certainly leads to a stronger conclusion than a nonsignificant
result. Specifically, a significant result provides the basis for a researcher to draw a
conclusion about the direction of an effect (see also Frick, 1996). Once this
conclusion is reached, the experimenter may wish to try variations on the
experiment to determine the boundary conditions of the effect or to see if the size
of the effect depends on other factors. A nonsignificant result is an inconclusive
result. As such, it does not support or confirm the null hypothesis. Instead, it fails
to determine conclusively the direction of the effect.

10. As an aside, if significant results were called "conclusive results" then the
propensity of researchers to accept the null hypothesis implicitly would be
diminished. Instead of reporting "the difference between means was not
significant" researchers would report "the direction of the difference between
means was not determined conclusively."


11. The negative consequences of using significance testing in editorial decision
making have been discussed for some time (Bakan, 1966; Greenwald, 1975) . We
believe the most serious problem is the incompatibility between significance
testing and effect-size estimation. Specifically, if significant results are a criterion
for publication, then published articles will contain inflated estimates of effect size
(cf. Hedges, 1984; Lane & Dunlap, 1978). Since the power of psychological
experiments is often relatively low (Cohen, 1962), this inflation can be substantial.
For example, Lane and Dunlap found that when the true difference between two
groups (each with n=3D10) was 8 IQ points and alpha was set at .05, the observed
mean difference between the two groups was over 18 points when only significant
results were considered. Naturally, as the alpha level was decreased (made more
stringent), the amount of overestimation of the true difference rose dramatically.
There have been some methods proposed for dealing with the bias inherent in
our current system. Rosenthal (1979) presented a procedure for addressing the "file
drawer" problem in meta-analytic research where only significant results are
included. He showed that as the number of published studies increases, the
probability of drawing incorrect conclusions by failing to include nonsignificant
unpublished studies becomes trivial. However, Rosenthal's analysis does not
speak to the issue of bias in estimating effect size. Hedges (1984) developed an
analytical procedure for estimating effect size based on a set of estimates from a
distribution truncated by including only significant results. Although Hedges's
procedure is a major contribution, it is not a perfect solution. For example,
complications arise when some but not all of the published articles report
significant results. Hedges recommends that in such situations, the nonsign ificant
results be discarded and his procedure applied to the significant outcomes.
Although this is a generally good solution, there are occasions in which this would
result in an unacceptably large amount of information being lost. Moreover,
complex situations such as one in which the probability of a paper being accepted
is a continuous monotonic function of the probability level are difficult to accommodate. In
short, no method for correcting for a bias can be quite as good as not having the
bias in the first place.

12. The bias would be eliminated by basing editorial decisions solely on the basis
of a paper's introduction and method section. However, there are two problems
with this approach. First, as pointed out by Lane and Dunlap (1978), an experiment
based on an unconventional theoretical perspective would not be very interesting if
the data contradicted the theory. Second, studies with conclusive results will (and
should) be preferred to studies with inconclusive results. Consider an editor who,
due to limited (and expensive) journal space, can only accept one of two papers
being considered. Both papers address equally-important topics using equal ly
rigorous methods. Paper 1 seeks to determine the relative effectiveness Conditions
A and B while Paper 2 seeks to determine the relative effectiveness of Conditions
C and D. In Paper 1, Condition A is significantly better than Condition B,
allowing the conclusion that, in the population, Condition A is better than
Condition B. In paper 2, Condition C is better, but not significantly better, than
Condition D. Paper 2, therefore, is unable to conclude which condition is better in
the population. The editor is faced with choosing between a conclusive experiment
and an inconclusive one. There seems little doubt that the conclusive experiment
should have a higher priority.

13. Although only one would be accepted, both of the above papers are worthy of
publication in the sense that they contain contributions to the field. A scientist
doing a meta-analysis of the difference between Conditions C and D would
certainly be interested in the results of Paper 2 even though the results of that paper
do not stand on their own. Moreover, if other experiments are conducted
comparing Conditions C and D and only the significant ones are published, the true
difference between these conditions will be vastly overestimated (Hedges, 1984;
Lane & Dunlap, 1978). Nonetheless, given the premium on journal space, the first
paper would have priority over the second. Editorial decision making must involve
judging the contribution of one paper relative to the contributions of other papers
vying for publication. Since a major aspect of a paper's contribution is the
conclusiveness of its results, statistical significance necessarily plays a critical role
in the decision process.


14. A recent article in Science reports on the huge explosion in electronic
publishing in the physical sciences (Taubes, 1996): At the end of 1995, over 100
peer-reviewed science journals were available over the internet. Some of these
journals use electronic publishing as a supplement to their regular paper
publication. However, an increasing number of journals are becoming strictly on-
line publications. For example, the psychology journal Psycoloquy has been
around for several years and is only available in electronic form.
A critical difference between electronic and paper publications is that the
marginal cost of an electronically-published article is negligible. It is known from
microeconomic theory that a firm should continue to increase production as long as
the marginal revenue is greater than the marginal cost. In the present context, this
means that a paper should be published as long as an article makes a positive
contribution to the field. Therefore, unlike the present publication system where
the contributions of papers are judged relative to contributions of other papers,
electronic publishing allows papers to be judged on their own merit. A well-
designed study producing inconclusive results makes a positive contribution to the
field and should be published. Since the cost of making this information available
to the research community is negligible, it is hard to justify keeping the
information from being disseminated.

15. The policy of ignoring the outcome of significance tests would be of great
benefit. Researchers could use significance tests as a short hand for whether a
confidence interval contains zero. However, they would be encouraged to refer to
these as "conclusiveness" tests thus avoiding two potential misuses of significance
testing: (a) using significance testing as a measure of effect size and (b) accepting
the null hypothesis when it is not rejected.

16. Naturally, researchers interested in estimating effect size would find the
elimination of significance testing as a criterion for publication highly desirable.
Schmidt's concern that significance testing is hindering the accumulation of
scientific knowledge would be addressed.

17. There are a number of other benefits of on-line publications. One important
benefit is speed of publication. By eliminating the production phase, it is possible
to go from submission to publication in a manner of weeks rather than months.
Another advantage is the ability to search the journal using key words and phrases.
It is also possible for articles to provide links to related articles or data throughout
the text or in the reference section. Authors could provide a link to the raw data
used in the study. This would allow scientists to double check the work and
replicate the analyses.

18. Several objections could be raised to the proposition that journals should be
published electronically and that these electronic journals should not use
significance testing as a criterion for publication. One objection is based on what
Greenwald (1975) calls the cultural truism that "... incompetence is more likely to
lead to erroneous nonsignificant, 'negative,' or null results." (p. 2). In refuting
this "cultural truism," Greenwald acknowledges that incompetence can have the
effect of introducing noise into the data. For example, an incompetent
experimenter could increase error variance by making random errors in data
transcribing, by running the experiment in an environment with distracting noise,
or by inaccurately placing electrodes. However, as Greenwald points out, o ther
more common types of incompetence result in systematic errors and thus a
tendency to falsely reject the null hypothesis. Examples include demand
characteristics, nonrandom sampling, invalid or contaminated manipulations, and
apparatus malfunctions.

19. A second objection to eliminating significance testing as a criterion for
publication is that so many studies would be published that there would be an
information overload. We believe a policy that rejects valid studies simply
because their publication would make it more difficult for researchers to stay
current would be a mistake. Although publishing more papers would certainly
require some adjustments such as increasing the number of articles that are
literature reviews and/or meta-analyses, we believe the problems would be
relatively minor.

20. The single largest barrier to electronic publishing is probably the attitude of
the academic community itself. It is clear that articles published in new on-line
journals would not carry the same weight as those published in more established
print journals. It is probably a matter of time, however, before attitudes towards
on-line publications change and the major journals are published electronically.
Electronic publishing will need to maintain a high level of quality control and
editorial oversight. The major difference is that the quality control will be focused
more on the theoretical justification and experimental methods and less on the
outcome of significance tests.


21. All indications are that the debate over significance testing will continue for
some time. Our position is that until all meritorious studies can be publis hed, the
present system for deciding which studies are more worthy of publication is
necessary. Significance testing allows one to make statements about the
conclusiveness of results and, therefore, in spite of the adverse consequences of
doing so, significance testing should continue to be used as an important criterion
in editorial decision making.

22. We propose that electronic publishing may provide the answer to the current
dilemma. By decreasing the marginal costs of publication, practically all
theoretically sound and well-designed studies can be published without regard to
the statistical significance of the results. This should decrease the emphasis on
null-hypothesis testing as well as increase the validity of meta-analyses. Given the
current rate of growth on the internet, this vision may soon be practical.


An earlier version of this paper was presented at the 12th Annual
Conference of the Society for Industrial and Organizational Psychology, April,
1997. St. Louis, MO. We thank Larry James for his helpful comments on an
earlier version of this manuscript.


Bakan, D. (1966). The test of significance in psychological research.
Psychological Bulletin, 66, 432-437.

Chow, S.L. (1988). Significance test or effect size? Psychological Bulletin,
103, 105-110.

Chow, S.L. (1996). Statistical significance: Rationale, validity, and utility.
Thousand Oaks, CA: Sage.

Cohen, J. (1962). The statistical power of abnormal-social psychological
research: A review. Journal of Abnormal and Social Psychology, 65, 145-153 .

Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49,

Frick, R.A. (1996). The appropriate use of null hypothesis testing.
Psychological Methods, 1, 379-390.

Greenwald, A.G. (1975). Consequences of prejudice against the null
hypothesis. Psychological Bulletin, 82, 1-20.

Hedges, L.V. (1984). Estimation of effect size under nonrandom sampling:
The effects of censoring studies yielding statistically insignificant mean
differences. Journal of Educational Statistics, 9, 61-85.

Lane, D.M. & Dunlap, W.P (1978). Estimating effect size: Bias resulting
from the significance criterion in editorial decisions. British Journal of
Mathematical and Statistical Psychology, 31, 107-112.

Rosenthal, R. (1979). The "file drawer problem" and tolerance for null
results. Psychological Bulletin, 86, 638-641.

Schmidt, F.L. (1996). Statistical significance testing and cumulative
knowledge in psychology: Implications for training researchers. Psychological
Methods, 1, 115-129.

Taubes, G. (1996). Science journals go wired. Science, 271, 764-766.