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Topic: Type I error

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	Probability of error (Site not responding. Last check: 2007-11-06)
		Type I errors which consist of rejecting a null hypothesis that is true; this amounts to a false positive result.
		Type II errors which consist of failing to reject a null hypothesisthat is false; this amounts to a false negative result.
		For a Type I error, it is shown as α (alpha) and is known as the size of the test and is 1 minus the specificity of the test.
	www.therfcc.org /probability-of-error-218090.html (184 words)

	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.
		A type I error is analogous to a false-positive result during diagnostic testing: A difference is shown when in "truth" there is none.
		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)

	Type I and Type II Errors
		In a sense, a type I error is twice as bad as a type II error.
		This emphasis on avoiding type I errors, however, is not true in all cases where hypothesis testing is done.
		Note that a type I error is often called alpha and is equal to the p-value.
	intuitor.com /statistics/T1T2Errors.html (1524 words)

	Windows Sockets Error Codes [Winsock]
		This error is returned from operations on nonblocking sockets that cannot be completed immediately, for example recv when no data is queued to be read from the socket.
		This error is returned if an incorrect protocol is explicitly requested in the socket call, or if an address of the wrong family is used for a socket, for example, in sendto.
		This error occurs if an application attempts to bind a socket to an IP address/port that has already been used for an existing socket, or a socket that was not closed properly, or one that is still in the process of closing.
	msdn.microsoft.com /library/en-us/winsock/winsock/windows_sockets_error_codes_2.asp?frame=true (2118 words)

	Type I error - Wikipedia, the free encyclopedia
		In statistical hypothesis testing, a Type I error consists of rejecting a null hypothesis that is true, It is the equivalent of a "false positive" or "false alarm".
		A test with high specificity has few Type I errors.
		The symbol for the probability of a Type I error is α (alpha) and is sometimes described as the size of the test.
	en.wikipedia.org /wiki/Type_I_error (95 words)

	Type I and II errors (1 of 2) (Site not responding. Last check: 2007-11-06)
		There are two kinds of errors that can be made in significance testing: (1) a true null hypothesis can be incorrectly rejected and (2) a false null hypothesis can fail to be rejected.
		The former error is called a Type I error and the latter error is called a Type II error.
		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.
	www.davidmlane.com /hyperstat/A18652.html (170 words)

	CLHS: Function ERROR
		As a consequence of calling invoke-debugger, error cannot directly return; the only exit from error can come by non-local transfer of control in a handler or by use of an interactive debugging command.
		Signals an error of type type-error if datum and arguments are not designators for a condition.
		Those kinds of errors, while beyond the scope of the condition system to formally model, are not beyond the scope of things that should seriously be considered when writing code that could have the kinds of sweeping effects hinted at by this example.
	www.lisp.org /HyperSpec/Body/fun_error.html (439 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)

	Type 2 Error
		Type 2 Error – If the null hypothesis is false, and we choose not to reject it on the evidence provided by our data, we are making a Type 2 Error.
		A Type 2 Error would occur if it were concluded that the two drugs produced the same effect, that is, there is no difference between the two drugs on average, when in fact they produced different ones.
		The probability of a type 2 error is symbolized by b and written as: P(type 2 error) = b (but is generally unknown).
	www.ag.unr.edu /gf/dm/_apst663_disc2/0000001a.htm (651 words)

	Type I error (Site not responding. Last check: 2007-11-06)
		In this case, a type I error would occur when temperature is thought to not equal to 43 when it actually equaled 43.
		The brewery owner would be in error because he believes that he is right when actually the manufacturer is right.
		In this case, a type II error would occur when temperature is thought to equal to 43 when it actually doesn’t equal 43.
	faculty.inverhills.mnscu.edu /dthomas/typeoneandtwoerrors.htm (241 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.
		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.
		It is the probability of a type I error and is set by the investigator in relation to the consequences of such an error.
	www.stats.gla.ac.uk /steps/glossary/hypothesis_testing.html (2225 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.childrensmercy.org /stats/ask/typetwo.asp (348 words)

	glossary
		Type of lymphocyte that, on simulation with an appropriate antigen, gives rises to a population of sensitised lymphocytes with receptor sites for that antigen on their surface membranes; so called because they are processed through the thymus; of primary importance in cell-mediated immunity.
		A type of flagellated protozoan, which inhabits the gastrointestinal or urinary tract; one type causes an extremely common sexually transmitted disease called trichomoniasis.
		In the statistical test of an hypothesis the error incurred by accepting the null hypothesis when the null hypothesis is false and some alternative to the null hypothesis is true.
	www.pestmanagement.co.uk /lib/glossary/glossary_t.shtml (4049 words)

	eBMJ -- Statistics at Square One: Differences between means: type I and type II errors and power (Site not responding. Last check: 2007-11-06)
		We saw in Chapter 3 that the mean of a sample has a standard error, and a mean that departs by more than twice its standard error from the population mean would be expected by chance only in about 5% of samples.
		If we set the limits at twice the standard error of the difference, and regard a mean outside this range as coming from another population, we shall on average be wrong about one time in 20 if the null hypothesis is in fact true.
		A range of not more than two standard errors is often taken as implying "no difference" but there is nothing to stop investigators choosing a range of three standard errors (or more) if they want to reduce the chances of a type I error.
	www.bmj.com /collections/statsbk/5.shtml (2015 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)

	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)

	Type I error - Wikipédia
		Dina tes hipotesa statistik, Type I error nyaeta ditolakna null hypothesis lamun bener, dina basa sejen manggihkeun hasil nu miboga statistical significance dina waktu bener-bener kajadian.
		Hiji tes nu mibanda specificity luhur bakal saeutik ngabogaan Type I errors.
		Simbol keur probabiliti Type I error nyaeta α (alpha) sarta kadang-kadang dipake salaku "ukuran" tes.
	su.wikipedia.org /wiki/Type_I_error (69 words)

	Type I and II errors (2 of 2) (Site not responding. Last check: 2007-11-06)
		Type I and II errors (2 of 2)
		A Type I error, on the other hand, is an error in every sense of the word.
		The Type I error rate is almost always set at.05 or at.01, the latter being more conservative since it requires stronger evidence to reject the null hypothesis at the.01 level then at the.05 level.
	www.ruf.rice.edu /~lane/hyperstat/A2917.html (191 words)

	Dorlands Medical Dictionary
		systematic error, reproducible inaccuracy; error in a measurement process that is predictable or in the same direction in all measurements; it may not be detectable by statistical methods.
		Type I error, in a hypothesis test, the rejection of the null hypothesis when it is true; the probability of a Type I error (the significance level) is denoted by
		Type II error, in a hypothesis test, failing to reject the null hypothesis when it is false; the probability of a Type II error is denoted by
	www.mercksource.com /pp/us/cns/cns_hl_dorlands.jspzQzpgzEzzSzppdocszSzuszSzcommonzSzdorlandszSzdorlandzSzdmd_e_14zPzhtm (2445 words)

	TCU Discuss System
		The first is that Type I error is when you think you have found an effect (due to independant variable manipulation) that doesn't really exist.
		Type II error is when you think that your manipulation did not cause a difference between the two groups (that the means are roughly equal) when in fact, your manipulation did affect the means somewhat.
		In response to Burton's comment earlier on Type II Errors: A Type II Error occurs when the null hypothesis (Ho) is accepted eventhough it is false, proving the alternative hypothesis (Ha) to be true.
	www2.tcu.edu /depts/discuss/messages/14/165.html (927 words)

	What values of alpha and power should I pick?
		A Type II error is concluding that a drug is ineffective, when it fact it is effective.
		A Type II error occurs when you conclude that a drug has no statistically significant effect, when in fact the drug is effective.
		In this case, the consequences of both a Type I and Type II error are pretty bad, so you set alpha to a small value (say 0.01) and power to a large value (perhaps 99%).
	www.graphpad.com /library/BiostatsSpecial/article_152.htm (690 words)

	Type I and II error (Site not responding. Last check: 2007-11-06)
		This is also referred to as the probability of a Type I error (reject H0 when H0 is true), hence the probability of a Type I error is a conditional probability (i.e., conditioned on the null hypothesis being true).
		The probability of a Type I error has been well defined for all of the tests of hypothesis that we have done.
		This is readily obtained from the product rule by multiplying the probability of a Type I error (which is the probability of dianosing as sick conditioned on the person being healthy) by the probability that a randomly chosen person is healthy (which must be known -- i.e., given to you in the problem).
	www.cs.uni.edu /~campbell/stat/IandII.html (427 words)

	Type I and Type II errors
		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.htm (4139 words)

	New View of Statistics: Type I & II Errors
		You can be responsible for a false alarm or Type I error, and a failed alarm or Type II error.
		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)

	Inferential Statistics (Site not responding. Last check: 2007-11-06)
		The probability of committing a type I error is always equal to alpha.
		In certain circumstances it may be possible to attach 'costs' to the two types of error, for example when a type I error could result in damage to patients.
		A Type I error means that we will begin a costly eradication of the United Kingdom's most significant population of this mammal even though it is doing no harm.
	obelia.jde.aca.mmu.ac.uk /resdesgn/inferent.htm (1783 words)

	Studium - Your Online Hobby Magazine
		As a continuation of the collecting of mint error coins from Issue #6, we move to the more dramatic examples which have in some cases mysteriously found their way out of the United States mint.
		What makes this type even more desirable is the appearance of more than one date, as well as the presence of the reverse image on the strikings (in lieu of uniface strikes, which are more common).
		The cap error coin is an extremely spectacular error type which is quite scarce and virtually unavailable in larger denominations due to their smaller mintages.
	www.studium.com /10/counterfeits.html (747 words)

	Type I error
		In statistical hypothesis testing, a Type I error consists of rejecting a valid null hypothesis as invalid.
		The symbol for the probability of a Type I error is $\alpha$ (alpha).
		The text of this article is licensed under the GFDL.
	www.ebroadcast.com.au /lookup/encyclopedia/ty/Type_I_error.html (50 words)

	A Testing Procedure
		Type I Error: We declare the new drug is more effective than the current drug on the market, when really it is not more effective.
		Type II Error: We declare the new drug is not more effective when it really is more effective.
		Of course, a Type II error is serious, also, because you have missed something which is better.
	www.stat.wmich.edu /s160/book/node51.html (394 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)

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