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Topic: Type II error

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	ECP - Primer on Type I and Type II Errors
		Note that a type I error is only possible in a positive study, and a type II error is possible only in a negative study.
		Type II errors are generally the result of a researcher studying too few participants.
		To avoid the error, some researchers perform a sample size calculation before beginning a study and, as part of the calculation, assert what a "true difference" is and accept that they will miss it 10% to 20% of the time (i.e., type II error rate of 0.1 or 0.2).
	www.acponline.org /journals/ecp/novdec01/primer_errors.htm (859 words)

	New Page
		Type II error could have been made when judges were assigned to measure the probabilities based on the evidence of ponderance, because each of them could have failed to notice that the evidence was actually false.
		As with Type I error, the smaller the type II error, the less likely it is that you were wrong and that the null hypothesis was false (the alternative hypothesis was correct).
		Type II error, which is also called the beta error, is there is failure to reject a null hypothesis that is false in hypothesis testing.
	business.fullerton.edu /isds/hani/Courses/MSIS_361A/TypeII_1.htm (6516 words)

	Type I and Type II Errors
		In a sense, a type I error is twice as bad as a type II error.
		This is represented by the yellow/green area under the curve on the left and is a type II error.
		Note that a type I error is often called alpha and is equal to the p-value.
	www.intuitor.com /statistics/T1T2Errors.html (1524 words)

	Type I and type II errors - Wikipedia, the free encyclopedia
		Statistical error: the difference between a computed, estimated, or measured value and the true, specified, or theoretically correct value (see errors and residuals in statistics) that is caused by random, and inherently unpredictable fluctuations in the measurement apparatus.
		Type I error, also known as an "error of the first kind", an α error, or a "false positive": the error of rejecting a null hypothesis when it is the true state of nature.
		Type II error, also known as an "error of the second kind", a β error, or a "false negative": the error of not rejecting a null hypothesis when the alternative hypothesis is the true state of nature.
	en.wikipedia.org /wiki/Type_II_error (3760 words)

	Type I and II errors (1 of 2) (Site not responding. Last check: 2007-11-07)
		The former error is called a Type I error and the latter error is called a Type II error.
		A Type II error is only an error in the sense that an opportunity to reject the null hypothesis correctly was lost.
		It is not an error in the sense that an incorrect conclusion was drawn since no conclusion is drawn when the null hypothesis is not rejected.
	davidmlane.com /hyperstat/A18652.html (170 words)

	Type I and II error
		The probability of a type I error is the level of significance of the test of hypothesis, and is denoted by alpha.
		A type II error occurs when one rejects the alternative hypothesis (fails to reject the null hypothesis) when the alternative hypothesis is true.
		The probability of a type II error is denoted by beta.
	www.cs.uni.edu /~campbell/stat/inf5.html (853 words)

	Unit 8 Lecture Notes — Statistical Significance
		Type I error: You find a relationship between the variables when there really is not one.
		Type II error: You don’t find a relationship between the variables when there really is one.
		A Type I error would be saying the drug worked (there was a relationship between taking the drug and improved health) when the drug really did not result in improved health.
	carbon.cudenver.edu /~ldeleon/lectr10(rev).html (1832 words)

	Stats: Type II error
		A type I error is rejecting the null hypothesis when the null hypothesis is true.
		A type II error is accepting the null hypothesis when the null hypothesis is false.
		In these situations, a Type II error might be a possible explanation for the negative study results.
	www.childrens-mercy.org /stats/ask/typetwo.asp (348 words)

	Statistics Glossary - hypothesis testing
		A type I error is often considered to be more serious, and therefore more important to avoid, than a type II error.
		If we do not reject the null hypothesis, it may still be false (a type II error) as the sample may not be big enough to identify the falseness of the null hypothesis (especially if the truth is very close to hypothesis).
		For any given set of data, type I and type II errors are inversely related; the smaller the risk of one, the higher the risk of the other.
	www.stats.gla.ac.uk /steps/glossary/hypothesis_testing.html (2225 words)

	Type II Errors (Site not responding. Last check: 2007-11-07)
		Type I errors are from rejecting Ho (the null hypothesis) when it is true.
		Type II errors come from accepting Ho when Ha is true (and therefore Ho is wrong).
		The greater is a, the smaller the acceptance region for Ho, and therefore the smaller the overlap and the smaller the probability of Type II error.
	umanitoba.ca /faculties/arts/economics/cameron/317_8/TypeIIErrors.htm (824 words)

	Univariate Statistics - Decision Errors and Power
		Type I error (Alpha) is the probability of rejecting the null hypothesis when the null is true; it is the probability of rejecting the null when you should really have failed to reject (FTR); it's saying there is a significant effect when there truly is none.
		Type II error (Beta) is the probability of failing to reject the null when the null is not true; it is the probability of failing to reject the null when you really should; ils the probability of saying there is no significant effect when there really is one.
		In your null hypothesis you state that the mean number of episodes watched in the general population is 50, with a standard error of 5.
	www.uwsp.edu /psych/cw/statmanual/power.html (1935 words)

	TYPE I AND TYPE II ERRORS
		A. A Type I error would be committed if it is concluded the water is safe when in reality the water is contaminated.
		A Type II error would be committed if it is concluded the water is not safe when in fact the water is safe.
		This error is not as serious as a Type I error.
	www.nku.edu /~statistics/Type_I_and_Type_II_Errors.htm (378 words)

	Chapter 13 - Hypothesis Testing
		It is the probability of a Type I error and is set by the investigator in relation to the consequences of such an error.
		A type II error is also called an error of the second kind.
		A type II error is frequently due to sample sizes being too small.
	www-rohan.sdsu.edu /~renglish/470/notes/chapt13/chapter13.htm (1307 words)

	[No title]
		A Type I error is defined as rejecting a true null hypothesis (not being a believer in the utility of testing point null hypotheses, what I really mean here is rejecting a null hypothesis that is so close to true that for practical purposes it is true).
		A Type I error is concluding that the drug is effective when in fact it is not.
		A Type II error is concluding that the drug is not effective when in fact it is.
	core.ecu.edu /psyc/wuenschk/StatHelp/Type-I-II-Errors.txt (4041 words)

	[No title]
		In this type of ternary graph, the 3-dimensional surface (fitted to a four-coordinate data set) is projected onto a 2-dimensional plane as an area contour.
		We propose the term Type V sums of squares to denote the approach that is widely used in industrial experimentation, to analyze fractional factorial designs; these types of designs are discussed in detail in the 2**(k-p) Fractional Factorial Designs section of the Experimental Design chapter.
		So-called Type I censoring describes the situation when a test is terminated at a particular point in time, so that the remaining items are only known not to have failed up to that time (e.g., we start with 100 light bulbs, and terminate the experiment after a certain amount of time).
	www.statsoft.com /textbook/glost.html (2273 words)

	Power analysis
		This rule implies that a Type I error is four times as costly as a Type II error.
		Thus, pursuing higher power at the expense of inflating Type I error seems to be a reasonable course of action.
		Ottenbacher (1996) pointed out that the relationship between power and Type II error is widely discussed, but the fact that low statistical power can reduce the probability of successful research replication is overlooked.
	www.creative-wisdom.com /teaching/WBI/power_es.shtml (1965 words)

	Type I error, type II error
		I then gave examples to examples to explain the difference between the two types of error one can make when doing a signifcance test.
		Type I error is the error made when the null hypothesis is rejected when in fact the null hypothesis is true.
		Type II error is the error made when the null hypothesis is not rejected when in fact the alternative hypothesis is true.
	www-stat.stanford.edu /~susan/courses/s60/split/node100.html (141 words)

	Statistical Power
		Random sampling error has produced a result that is pure noise but looks indistinguishable from the situation on the right in which the alternate hypothesis is true and there is no error.
		In plain words this means that the odds of a Type I error (alpha) are quite independent from the odds of a Type II error (beta).
		It should now be clear that the possibility of error is always present, and that the risk of Type I errors is not independent of the risk of Type II errors.
	web.uct.ac.za /depts/psychology/psy300/lectur18.html (1631 words)

	STATISTICAL ERRORS (TYPE I, TYPE II, POWER) (Site not responding. Last check: 2007-11-07)
		The probability of a Type II error is 3/20 = 15%.
		A Type II error would involve declaring the person innocent when he is guilty.
		One could argue that a Type II error should be minimized here if one agrees that spending time and money on a useless drug would replace what might be some other effective treatment.
	www.herkimershideaway.org /writings/type12.htm (792 words)

	Glossary of Terms
		Errors result from unintentional activation of inappropriate schemas, loss of activation through interruption or forgotten intention, or faulty triggering of active schemas -- either false triggering by a similar cue, or failure to trigger.
		The error occurs when an action that would be correct in one mode is executed while unwittingly in another mode.
		The error in using the brakes is pressing too hard on the brake pedal causing a skid.
	www.mistakeproofing.com /Glossary/glossary.html (4766 words)

	New View of Statistics: Type I & II Errors
		The Type II error needs to be considered explicitly at the time you design your study.
		The power of the study is sometimes referred to as 80% (or 90% for a Type II error rate of 10%).
		When you are looking at lots of effects, the near equivalent of inflated Type II error is the increased chance that any one of the effects will be bigger than you think it could be (bigger than its upper confidence limit).
	www.sportsci.org /resource/stats/errors.html (1481 words)

	The Sofia Open Content Initiative - Elementary Statistics (Site not responding. Last check: 2007-11-07)
		So, α = P(Type I error) = the probability that we think the average cost of dinner at the better restaurants in Silicon Valley is less than 25 dollars when, in fact, the cost is at least 25 dollars.
		So, β = P(Type II error) = the probability that we think the coin is fair when, in fact, it really is not fair.
		So, β = P(Type II error) = the probability that the average cost of dinner at the better restaurants in Silicon Valley is at least 25 dollars when, in fact, the cost is less than 25 dollars.
	sofia.fhda.edu /gallery/statistics/lessons/lesson09-5.html (690 words)

	criteria of search engine
		In short, a higher sensitivity means that it has less type I error, but it might has greater type II error, which causes over-prediction.
		Now that sensitivity only considers type I error and selectivity only type II error, to evaluate the overall performance of the search engine on behalf of TP, it comes natually that we can use the average of sensitivity and selectivity.
		As we see above, all the criteria focus on how to reflect the type I and type II error in the formula, because either of sensitivity and selectivity only reflects one factor of the errors, for an overall evaluation, they are averaged.
	brc.mcw.edu /MetaGene/General/Analysis/criteria.html (1260 words)

	The Hoosier Gadfly » Blog Archive » Intel chief oversees bureaucratic Type II error (Site not responding. Last check: 2007-11-07)
		In scientific research, there are two types of errors called, not surprisingly, Type I and Type II errors.
		In more common parlance, a Type I error is a “false positive” (like finding an innocent person guilty) whereas a Type II error is a “false negative” (like finding a guilty person innocent).
		Having been burned because of a Type I error, the natural tendency is to err in the other direction.
	paulhager.org /wordpress/?p=84 (1411 words)

	Type I and Type II errors
		Wuensch, K. Evaluating the relative seriousness of type I versus type II errors in classical hypothesis testing.
		The costs of the errors stay put, but the type II error probability as a function of the state of nature decreases.
		Some of the reduced cost should be used to reduce the type I error probability.
	core.ecu.edu /psyc/wuenschk/StatHelp/Type-I-II-Errors.htm (4139 words)

	Hypothesis Testing (Site not responding. Last check: 2007-11-07)
		Recall also that we choose the probability of making a Type I error when we set Alpha and that if we decrease the probability of making a Type I error we increase the probability of making a Type II error.
		Thus, the probability of correctly retaining a true null has the same relationship to Type I errors as the probability of correctly rejecting an untrue null does to Type II error.
		The lower our Alpha the less likely we are to make a Type I error, but the more likely we are to make a Type II error.
	faculty.uncfsu.edu /dwallace/spower.html (511 words)

	Radiation Glossary S-T \| Radiation Protection \| US EPA (Site not responding. Last check: 2007-11-07)
		Tritium (chemical symbol H-3) is a radioactive isotope of the element hydrogen (chemical symbol H).
		a decision error that occurs when the null hypothesis is rejected when it is true.
		a decision error that occurs when the null hypothesis is accepted when it is false.
	www.epa.gov /radiation/terms/termst.htm (757 words)

	More on Type I and Type II Errors
		In the initial discussion on these errors, there was not an in-depth discussion of a Type II Error.
		This is the Type II Error: Failure to reject
		However, If is not rejected when it should be rejected, then a Type II Error has been made.
	home.xnet.com /~fidler/triton/math/review/mat170/beta/beta1.htm (494 words)

	acceptance error ; beta error ; β-error ; type II error ; error of second kind
		acceptance error ; beta error ; β-error ; type II error ; error of second kind
		error de segona especie ; error de tipus II ; error β ; error d'acceptació
		This Glossary may not be copied, reproduced or retained in any form whatsoever without the express permission of the ISI.
	isi.cbs.nl /glossary/term29.htm (307 words)

	Error (Type II)
		Error that concludes that someone is not guilty, when in fact, they really are.
		Error that concludes that someone is not guilty, when in fact, he or she really is. (accept Ho as true, beeing false, when Ha is true).
		BETA is the probabilty of error type II Also known as consumer's risk.
	www.isixsigma.com /dictionary/Error_(Type_II)-239.htm (221 words)

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