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Topic: Statistical power

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	Statistical power - Wikipedia, the free encyclopedia
		The power of a statistical test is the probability that the test will reject a false null hypothesis, or in other words that it will not make a Type II error.
		The higher the power, the greater the chance of obtaining a statistically significant result when the null hypothesis is false.
		Statistical power depends on the significance criterion, the size of the difference or the strength of the similarity (that is, the effect size) in the population, and the sensitivity of the data.
	en.wikipedia.org /wiki/Statistical_power (430 words)

	Power - Wikipedia, the free encyclopedia
		In power stations, also known as power plants, the energy in fossil fuels and nuclear energy is converted to electrical energy.
		Power is often used in supernatural senses to refer to the motivating forces behind different disciplines of magic.
		Power can also be used to refer to abilities specific to a character in a video or card game.
	en.wikipedia.org /wiki/Power (415 words)

	Statistical power -- Facts, Info, and Encyclopedia article (Site not responding. Last check: 2007-10-08)
		The higher the power, the greater the chance of obtaining a (Click link for more info and facts about statistically significant) statistically significant result when the null hypothesis is false.
		Statistical tests attempt to use data from (A small part of something intended as representative of the whole) samples to determine if differences or similarities exist in a ((statistics) the entire aggregation of items from which samples can be drawn) population.
		Statistical power depends on the significance criterion, the size of the difference or the strength of the similarity (that is, the (Click link for more info and facts about effect size) effect size) in the population, and the sensitivity of the data.
	www.absoluteastronomy.com /encyclopedia/s/st/statistical_power.htm (419 words)

	Power of a Hypothesis Test Applet (24-Mar-1997) (Site not responding. Last check: 2007-10-08)
		This applet illustrates the fundamental principles of statistical hypothesis testing through the simplest example: the test for the mean of a single normal population, variance known (the Z test).
		This hypothesis testing procedure is set up to give the null hypothesis ``the benefit of a doubt;'' that is, to accept the null hypothesis unless there is strong evidence to support the alternative.
		Todd Ogden, Dept. of Statistics, Univ. of South Carolina
	www.stat.sc.edu /~ogden/javahtml/power/power.html (359 words)

	Epidemiology for Journalists \| A Guide to Statistics and Data Analyses (Site not responding. Last check: 2007-10-08)
		P value (probability value) is another statistical measure that attempts to quantify uncertainty about whether an outcome is due to chance or whether it actually reflects a true difference.
		Statistical power is the probability that one can detect an effect if there really is one.
		Studies of the statistical power of the most popular cluster detection methods, however, often show that unless the risks in the hypothetically affected area were enormous, the analysis would not have revealed a cluster.
	www.facsnet.org /tools/ref_tutor/epidem/data.php3 (938 words)

	Statistical Power in Structural Equation Modeling
		The concept of power in statistical theory is defined as the probability of rejecting the null hypothesis given that the null hypothesis is false.
		Using an approach similar to that used in power studies of Hotelling's T-square, Kaplan and George (1995) found that power was most affected by the degree of true factor mean differences.
		Finally, with equal sample sizes, Kaplan and George (1995) found that the power of the Wald test of factor mean differences is relatively robust to violations of factorial invariance.
	www.gsu.edu /~mkteer/power.html (1480 words)

	[No title]
		This statistic is used to evaluate the statistical significance of parameter estimates computed via maximum likelihood methods.
		The term Statistical Process Control (SPC) is typically used in context of manufacturing processes (although it may also pertain to services and other activities), and it denotes statistical methods used to monitor and improve the quality of the respective operations.
		The statistical significance of a result is an estimated measure of the degree to which it is "true" (in the sense of "representative of the population").
	www.statsoftinc.com /textbook/gloss.html (8802 words)

	Standard 5-1 : NCES Statistical Standards
		Statistical analysis techniques must be used that are appropriate for the specific research question.
		The efficacy of individual statistical approaches depends on the assumptions of the techniques having been met; therefore, the assumptions underlying the techniques must be discussed.
		If a statistically significant difference for a total group under study is observed, but similar subgroup differences of the same magnitude are associated with smaller sample sizes and/or larger standard errors and are not statistically significant, this may be noted.
	nces.ed.gov /statprog/2002/std5_1.asp (1328 words)

	Statistical Power
		If you do a study in which the power is low, even if the research hypothesis is true, this study will probably not give statistically significant results.
		When the power of a planned study is found to be low, researchers look for practical ways to increase the power to an acceptable level.
		Power calculation for mean differences: one sample and two sample tests (see pp.
	www.chsbs.cmich.edu /k_han/psy511/power1.htm (369 words)

	Power Analysis
		In some power analysis software programs, a number of graphical and analytical tools are available to enable precise evaluation of the factors affecting power and sample size in many of the most commonly encountered statistical analyses.
		In most situations in statistical analysis, we do not have access to an entire statistical population of interest, either because the population is too large, is not willing to be measured, or the measurement process is too expensive or time-consuming to allow more than a small segment of the population to be observed.
		Intelligent analysis of power and sample size requires the construction, and careful evaluation, of graphs relating power, sample size, the amount by which the null hypothesis is wrong (i.e., the experimental effect), and other factors such as Type I error rate.
	www.statsoft.com /textbook/stpowan.html (5356 words)

	Statistical Power, Sample Size in Randomized Controlled Trials... [Peer review, July 13 JAMA. 1994;272:122-124] (c) AMA ...
		Power to detect 25% and 50% relative differences was calculated for the subset of trials with negative results in which a simple two-group parallel design was used.
		The relationship between negative findings (ie, when statistical significance was not reached) and statistical power has been well illustrated in Freiman and colleagues'[2] review of 71 RCTs with negative results published during 1960 to 1977.
		Their review indicated that most of the trials had low power to detect these effects: only 7% (5/71) had at least 80% power to detect a 25% relative change between treatment groups and that 31% (22/71) had a 50% relative change, as statistically significant (alpha=.05, one tailed).
	www.ama-assn.org /public/peer/7_13_94/pv3037x.htm (2686 words)

	PASS 2002 - Power Analysis and Sample Size Software. Design of Experiments. Experimental Design. Scientific Studies. N.
		A statistical test's power is the probability that the test procedure will result in statistical significance.
		Power is related to the sample size, the size of the type 1 (alpha) error, the actual size of the effect, and the size of experimental error.
		As statistical significance is usually the desired outcome, planning and running a study to achieve a high power is of prime importance to the researcher.
	www.ncss.com /pass.html (559 words)

	Statistical Power (Site not responding. Last check: 2007-10-08)
		In statistical hypothesis testing, we use data to make a decision about whether to reject a statistical hypothesis (usually stated as a null hypothesis) in favor of an alternative hypothesis.
		The approach is then to calculate a test statistic from the data (e.g., a t-test) and compare its value to the statistics distribution assuming that the null hypothesis is true.
		The third “ingredient” in power is the amount of variability that occurs in the test statistic.
	fisher.forestry.uga.edu /popdyn/Power.html (1151 words)

	A REVIEW OF STATISTICAL POWER ANALYSIS SOFTWARE
		Statistical power is the probability of getting a statistically significant result given that there is a biologically real effect in the population being studied.
		Power analysis can distinguish between these alternatives, and is therefore a critical component of designing experiments and testing results (Toft and Shea 1983, Rotenberry and Weins 1985, Peterman 1990, Fairweather 1991, Taylor and Gerrodette 1993, Thomas and Juanes 1996).
		Such "prospective" power analyses are usually exploratory in nature, investigating the relationship between the range of sample sizes that are deemed feasible, effect sizes thought to be biologically important, levels of variance that could exist in the population (usually taken from the literature or from pilot data), and desired levels of
	www.zoology.ubc.ca /~krebs/power.html (6517 words)

	Statistical Power
		Statistics is used to manage the uncertainty that arises from not knowing the true state of nature.
		His treatment of power is mainly in terms of the formal statistics, and may be more difficult than Trochim's definition: the odds that you observe an effect when it occurs.
		Thus power counteracts the threat of a Type I error: The inevitable (albeit manageable) risk that a decision to reject the null hypothesis could be wrong is countered by the power to ensure that it will be right.
	web.uct.ac.za /depts/psychology/psy300/lectur18.html (1631 words)

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		statistic is an unbiased estimate of the population effect size.
		Statistical effect size is a measure of the standardized parameter estimate in the model which is used to obtain the
		Statistical power has long taken a distant second place to the statistical significance of the test statistic.
	lbc.nimh.nih.gov /fidap/fmripower.html (2379 words)

	STATISTICAL POWER AND SAMPLE SIZE
		A test that yields P <.001, for example, asserts that the probability of rejecting the Null when it is actually true is less than one chance in a thousand.
		A Type II error, on the other hand, occurs when the Null really is false but the statistical test fails to reject it.
		The power of the test is the probability that the Null will indeed be rejected when some alternative hypothesis is true.
	web.psych.ualberta.ca /~wahlsten/power.html (353 words)

	Statistical Power Analysis
		Power is broadly defined as "the probability that a statistical significance test will reject the null hypothesis for a specified value of an alternative hypothesis." Another way to define it is "the ability of a test to detect an effect, given that the effect actually exists."
		Power analysis then becomes a useful tool to determine if sufficient power exists (2) for specified values of (1), (3), (4), and (5).
		The situation with high power is the reverse: you will likely see very significant results, even if the size of the effect you're investigating is not practical.
	cc.uoregon.edu /cnews/summer2000/statpower.html (1399 words)

	Statistical Power From William M
		The logic of statistical inference with respect to these components is often difficult to understand and explain.
		The goal is to achieve a balance of the four components that allows the maximum level of power to detect an effect if one exists, given programmatic, logistical or financial constraints on the other components.
		As you increase power, you increase the chances that you are going to find an effect if it’s there (wind up in the bottom row).
	www.people.vcu.edu /~pdattalo/702SuppRead/PowrAnal.html (1527 words)

	Book: Statistical Power Analysis (Site not responding. Last check: 2007-10-08)
		This authored book presents a simple and general method for conducting statistical power analysis based on the widely used F statistic.
		The previous edition was the first book to discuss in detail the application of power analysis to both traditional null hypothesis tests and to minimum-effect testing.
		Ideal for students and researchers of statistical and research methodology in the social, behavioral, and health sciences who want to know how to apply methods of power analysis to their research.
	www.assess.com /Books/b-45259.htm (449 words)

	Amazon.com: Books: How Many Subjects? : Statistical Power Analysis in Research (Site not responding. Last check: 2007-10-08)
		Statistical Power Analysis for the Behavioral Sciences by Jacob Cohen
		Power Analysis for Experimental Research : A Practical Guide for the Biological, Medical and Social Sciences by R.
		Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests by Kevin R. Murphy
	www.amazon.com /exec/obidos/tg/detail/-/0803929498?v=glance (646 words)

	New Scientist Breaking News - Key GM crop experiment 'lacks statistical power'
		From the start activists periodically ripped up trial crops while others claimed farmers were biasing the outcome by treating GM fields with less herbicide than would be used commercially - a charge the trial scientists rejected.
		Now, with the endgame in sight, campaigners are keen to shift the focus onto what even some neutral experts see as the experiment's potential Achilles' heel: its statistical power.
		Peter Green, president of Britain's Royal Statistical Society, says that while many of the report's points about statistical power are valid, such problems are not unique to these trials and there are well-established ways of handling them.
	www.newscientist.com /article.ns?id=dn3547 (716 words)

	Re: Statistical Power Analysis (Site not responding. Last check: 2007-10-08)
		To perform a power analysis, only two pieces of information that are required, the change in means and the standard deviations.
		As I understand it, a Power Analysis should be performed during the pre-planning stages of a research study.
		In other words, they are computing the Power of their data after they have collected as much as they can or have time to, and then deciding if more should be collected to increase their "power".
	isb.ri.ccf.org /biomch-l/archives/biomch-l-2002-03/00102.html (479 words)

	ASQ: Using the Power of Statistical Thinking
		Knowledge of statistics alone is insufficient to provide the type of guidance needed for process ipmrovement.
		Critical components of improvement work are the management and technical knowledge needed to determine where and how to employ statistical techniques.
		This publication contains observations on the role of statistics and statisticians in providing guidance to industrial organizations in the use of statistical thinking.
	qualitypress.asq.org /perl/catalog.cgi?item=S0909 (145 words)

	Statistical Power (Site not responding. Last check: 2007-10-08)
		We had "null" hypothesis that assumes mean value of dices will be 3.0, the alternative assumes 3.5.
		Now as we discussed we know that if more elements are sampled then higher the power.
		The P553 Statistics is maintained with WebBBS 2.25.
	www.indiana.edu /~jkkteach/p553_bbs/p553.cgi?read=17 (586 words)

	Statistical Power (Site not responding. Last check: 2007-10-08)
		One of the most interesting introductions to the idea of statistical power is given in the 'OJ' Page which was created by Rob Becker to illustrate how the decision a jury has to reach (guilty vs. not guilt) is similar to the decision a researcher makes when assessing a relationship.
		The OJ Page uses the infamous OJ Simpson murder trial to introduce the idea of statistical power and illustrate how manipulating various factors (e.g., the amount of evidence, the "effect size", and the level of risk) affects the validity of the verdict.
		If you could make reasonable estimates of the effect size, alpha level and power, it would be simple to compute (or, more likely, look up in a table) the sample size.
	www.socialresearchmethods.net /kb/power.htm (1632 words)

	Statistical Calculators (Site not responding. Last check: 2007-10-08)
		These calculators extend the functionality of the old Xlisp-Stat based Power Calculator by not only computing the power for given sample size, or sample size for given power, but will also compute the other available items when specified.
		Rweb is a Web based interface to R (a statistical analysis package) that takes the submitted code, runs R on the code (in batch mode), and returns the output (printed and graphical).
		Based on CGI forms, it computes power for given sample size, or sample size for given power, in a large number of one-sample and two-sample situations.
	calculators.stat.ucla.edu (714 words)

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