When testing a research hypothesis, which the researcher has good reason to believe is true, it is customary to use a null hypothesis. Null hypothesis is typically a hypothesis of no difference or of no association between variables. If the research hypothesis is that men have a higher rate of suicide than do women, the null hypothesis would be that there is no difference in suicide rates between men and women. Researcher then try to disprove the null hypothesis and if they fail to reject it, they accept the research hypothesis.

**Large-Scale Simultaneous
Hypothesis Testing: The Choice of a Null Hypothesis** - Efron, Bradley

Abstract: Current scientific techniques in genomics and image processing routinely produce
hypothesis testing problems with hundreds or thousands of cases to consider
simultaneously. This poses new difficulties for the statistician, but also opens new
opportunities. In particular, it allows empirical estimation of an appropriate null
hypothesis. The empirical null may be
considerably more dispersed than the usual theoretical null distribution that would be
used for any one case considered separately. An empirical Bayes analysis plan for this
situation is developed, using a local version of the false discovery rate to examine the
inference issues. Two genomics problems are used as examples to show the importance of
correctly choosing the null hypothesis.

**The Proposal for a Null
Hypothesis Test and Search for New Physics in the Top Dilepton Sample**

Andrew Ivanov (University of Rochester), CDF Collaboration

We propose a null hypothesis test to search for new physics in top dilepton decays. We
used data collected by the CDF Run 2 experiment in proton-antiproton collisions at a
center-of-mass energy of 1.96 TeV at the Fermilab Tevatron. The test is based on a
comparison of several kinematic variables; the Kolmogorov-Smirnov statistic is used to
quantify the consistency of the variables' distributions in the data with those expected
from the production of t \bart and SM background. In this way we determine an overall
confidence level on the hypothesis that the Run 2 data can be explained by purely SM
physics.

**Statistical Power and the
Testing of Null Hypotheses: A Review of Contemporary Management Research and
Recommendations for Future Studies** - Luke H. Cashen, Louisiana State
University, Scott W. Geiger, University of South Florida St. Petersburg

The purpose of this study is to determine how well contemporary management research fares
on the issue of statistical power with regard to studies specifically predicting null
relationships between phenomena of interest. This power assessment differs from
traditional power studies because it focuses solely on studies that offered and tested
null hypotheses. A sample of studies containing hypothesized null relationships was taken
from five mainstream management journals over the 1990 to 1999 time period. Results of the
power assessment suggest that management researchers’ abilities to affirm null
hypotheses are low. On average, the power assessment revealed that for those studies that
found nonsignificance of results and consequently affirmed their null hypotheses, the
actual Type II error rate was nearly 15 times greater than what is advocated in the
literature when failing to reject a false null hypothesis. Recommendations for researchers
proposing and testing formal null hypotheses are also discussed.

**Sex and Gender
Comparisons: Does Null Hypothesis Testing Create a False Dichotomy?**

Olga Eizner Favreau, Departement de psychologie, University de Montreal.

In an ongoing debate about the value of doing tests for sex differences, those in favour
claim that if sex differences exist, it is important to know about them. However, the null
hypothesis (NH) tests that are used for inferring group differences can detect only mean
differences and provide no information about how the differences are distributed across
groups. Theoretical and empirical examples show how NH rejection can occur when only a
small proportion of individuals differ from all others, demonstrating that these tests are
incapable of supporting inferences to general group differences.

**When is it Acceptable to Accept a Null Hypothesis: No Way, Jose? **

Jose M. Cortina, George Mason University, Robert G. Folger, Tulane University

Previous research has suggested that there exists a bias in the social sciences against
no-effect hypotheses. This is regrettable given the importance of establishing not only
when an effect does occur but also the boundary conditions of that effect. The purposes of
this article are two-fold The first purpose is to review relevant portions of the history
of hypothesis testing in an attempt to identify the sources of bias against hypotheses of
no effect.

**Random walks in the
history of life** - James L. Cornette, and Bruce S. Lieberman

Departments of Geology and Ecology and Evolutionary Biology, University of Kansas,
Lawrence; and Department of Mathematics, Iowa State University

The simplest null hypothesis for evolutionary time series is that the observed data follow
a random walk. We examined whether aspects of Sepkoski's compilation of marine generic
diversity depart from a random walk by using statistical tests from econometrics.
Throughout most of the Phanerozoic, the random-walk null hypothesis is not rejected for
marine diversity, accumulated origination or accumulated extinction, suggesting that
either these variables were correlated with environmental variables that follow a random
walk or so many mechanisms were affecting these variables, in different ways, that the
resultant trends appear random. The only deviation from this pattern involves rejection of
the null hypothesis for roughly the last 75 million years for the diversity and
accumulated origination time series.

**Rushton's Defenders and Their Hasty Rejection of the Null Hypothesis**

Zack Z. Cernovsky, University of Western Ontario

Rosenthal and Rubin (1985) pointed out that in research on extreme situations (e.g., new
treatments for terninally ill patients) any noticeable statistical trend in the desirable
direction is valuable. Their approach is now being misused by those defending Rushton's
(1988) "theory" about American Blacks (based on weak trends in excessively
suspect data sets). Hasty and eager acceptance of weak, biased, and unrepresentative data
as scientific evidence of genetically based and relatively immutable racial differences in
human potential amounts to psychological warfare on oppressed racial groups. Similar
defamation of vulnerable minorities by Nazi pseudoscientists led to the loss of millions
of human lives in the past. Statistical theory classifies similar endeavors as a Type I
error (a misleading rejection of the null hypothesis).

**Testing the Null Hypothesis of Stationarity Against an Autoregressive Unit Root
Alternative** - Zhijie Xiao

We propose a new test for the null hypothesis that a time series is stationary around a
deterministic trend. The test is valid under general conditions on stationarity.
Asymptotic distributions of the test statistic are derived under both the null hypothesis
and the alternative hypothesis of a unit root. It is shown that the limiting distribution
has the classical Kolmogoroff– Smirnoff form. Critical values for the null
distribution are calculated. Consistency of the tests is proved.

**Null Hypothesis Significance Testing: Effect Size Matters**

Authors: Gliner J. A.; Vaske J. J.; Morgan G. A.

Abstract: A statistically significant outcome only indicates that it is likely that there
is a relationship between variables. It does not describe the extent (strength) of that
relationship. In this article, emphasis is placed on the importance of assessing the
strength of the relationship between the independent and dependent variables using effect
size indices.

**A Note on the Bandwidth
Choice When the Null Hypothesis is Semiparametric** - JORGE BARRIENTOS-MARIN,
Universidad de Alicante - Faculty of Economic and Business Sciences

Abstract: This work presents a tool for the additivity test. The additive model is widely
used for parametric and semiparametric modeling of economic data. The additivity
hypothesis is of interest because it is easy to interpret and produces reasonably fast
convergence rates for non-parametric estimators. Another advantage of additive models is
that they allow attacking the problem of the curse of dimensionality that arises in non-
parametric estimation. Hypothesis testing is based in the well-known bootstrap residual
process. In nonparametric testing literature, the dominant idea is that bandwidth utilized
to produce bootstrap sample should be bigger that bandwidth for estimating model under
null hypothesis.

**Reconsidering the null
hypothesis: Is maternal rank associated with birth sex ratios in primate groups?** -
Gillian R. Brown, and Joan B. Silk, Sub-Department of Animal Behaviour, Department of
Zoology, University of Cambridge; and Department of Anthropology, University of
California.

Trivers and Willard hypothesized that vertebrates adaptively vary the sex ratio of their
offspring in response to the mother's physical condition. This hypothesis has produced
considerable debate within evolutionary biology. Here we use meta-analysis techniques to
evaluate claims that nonhuman primate females facultatively adjust the sex ratio of their
progeny in relation to their own dominance rank in a uniform way.