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Statistically significant difference

Sociologyindex, Sociology Books 2012, Statistically significant difference

'Statistically significant difference' simply means there is statistical evidence that there is a difference. 

'Statistically significant difference' does not mean the difference is large, important or significant in the usual sense of the word.

When researchers study within groups or between group differences, they need a technique to determine if this difference would have occurred by chance.

Various statistical techniques can determine this and if it is unlikely the differences could have occurred by chance it is called a statistically significant difference.

Small and non-notable differences can be found to be statistically significant. Statistically significant result is not always of practical significance.

Usually a .05 level of significance is used (there are 5 chances of one hundred trials that this difference would occur by chance) but other levels can be used. When a researcher finds no statistically significant difference and writes as follows:

"we found no statistically significant difference," he could be misquoted as "they found that there was no statistically significant difference."

What is the probability of replicating a statistically significant effect?
Jeff Miller, University of Otago, Dunedin, New Zealand
Abstract: If an initial experiment produces a statistically significant effect, what is the probability that this effect will be replicated in a follow-up experiment? I argue that this seemingly fundamental question can be interpreted in two very different ways and that its answer is, in practice, virtually unknowable under either interpretation. Although the data from an initial experiment can be used to estimate one type of replication probability, this estimate will rarely be precise enough to be of any use. The other type of replication probability is also unknowable, because it depends on unknown aspects of the research context. Thus, although it would be nice to know the probability of replicating a significant effect, researchers must accept the fact that they generally cannot determine this information, whichever type of replication probability they seek.

The Orthopaedic Trauma Literature: An Evaluation of Statistically Significant Findings in Orthopaedic Trauma Randomized Trials - Jinsil Sung; Judith Siegel; Paul Tornetta; Mohit Bhandari
Abstract: Evidence-based medicine posits that health care research is founded upon clinically important differences in patient centered outcomes. Statistically significant differences between two treatments may not necessarily reflect a clinically important difference. We aimed to quantify the sample sizes and magnitude of treatment effects in a review of orthopaedic randomized trials with statistically significant findings.

Association between industry funding and statistically significant pro-industry findings in medical and surgical randomized trials - Mohit Bhandari, Jason W. Busse, Dianne Jackowski, Victor M. Montori, Holger Schünemann, Sheila Sprague, Derek Mears, Emil H. Schemitsch, Dianne Heels-Ansdell and P.J. Devereaux
Conflicting reports exist in the medical literature regarding the association between industry funding and published research findings. In this study, we examine the association between industry funding and the statistical significance of results in recently published medical and surgical trials.

Gender Has a Small but Statistically Significant Effect on Clearance of CYP3A Substrate Drugs - David J. Greenblatt, MD and Lisa L. von Moltke, MD
Address for reprints: David J. Greenblatt, MD, Department of Pharmacology and Experimental Therapeutics, Tufts University School of Medicine, 136 Harrison Ave, Boston
From the Department of Pharmacology and Experimental Therapeutics, Tufts University School of Medicine and Tufts Medical Center, Boston, Massachusetts. Dr. Hartmut Derendorf acted as editor for this article.
The role of gender on the disposition of drugs metabolized by cytochrome P4503A (CYP3A) remains controversial. Some sources suggest that CYP3A activity in women exceeds that in men, but evidence to support this position is inconsistent at best. We evaluated 38 data sets in which clearance of CYP3A substrate drugs was studied in healthy young male and young female subjects. None of these drugs was a substrate for transport by P-glycoprotein (P-gp). The overall mean (±SE) for the female/male ratio of weight-normalized clearance was 1.26 (±0.07) for parenteral dosage and 1.17 (±0.07) for oral dosage. Both ratios were significantly different (P < .05) from 1.0. For oral dosage studies, the female/male clearance ratio was unrelated to the drug's absolute oral bioavailability. Thus gender has a small and statistically significant, although most likely clinically unimportant, influence on CYP3A phenotype for substrates not transported by P-gp.

ELISA: Structure-Function Inferences based on statistically significant and evolutionarily inspired observations
Boris E Shakhnovich, John M Harvey, Steve Comeau, David Lorenz, Charles DeLisi
BioInformatics Program, Boston University, Boston, MA, 02215, USA
Eugene Shakhnovich, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
BMC Bioinformatics 2003, 4:34 The database is available at http://romi.bu.edu/elisa
Abstract: The problem of functional annotation based on homology modeling is primary to current bioinformatics research. Researchers have noted regularities in sequence, structure and even chromosome organization that allow valid functional cross-annotation. However, these methods provide a lot of false negatives due to limited specificity inherent in the system. We want to create an evolutionarily inspired organization of data that would approach the issue of structure-function correlation from a new, probabilistic perspective. Such organization has possible applications in phylogeny, modeling of functional evolution and structural determination. ELISA (Evolutionary Lineage Inferred from Structural Analysis, http://romi.bu.edu/elisa webcite) is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function "neighborhoods". The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). We introduce a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind.

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