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about a relationship in your observations. You can essentially make two kinds of errors about relationships: conclude that there is no relationship when in fact there is (you missed the relationship or didn't see error rate calculation it) conclude that there is a relationship when in fact there is not (you're error rate running record seeing things that aren't there!) Most threats to conclusion validity have to do with the first problem. Why? Maybe it's

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because it's so hard in most research to find relationships in our data at all that it's not as big or frequent a problem -- we tend to have more problems finding the needle

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in the haystack than seeing things that aren't there! So, I'll divide the threats by the type of error they are associated with. Finding no relationship when there is one (or, "missing the needle in the haystack") When you're looking for the needle in the haystack you essentially have two basic problems: the tiny needle and too much hay. You can view this as a signal-to-noise ratio problem.The "signal" raw read error rate is the needle -- the relationship you are trying to see. The "noise" consists of all of the factors that make it hard to see the relationship. There are several important sources of noise, each of which is a threat to conclusion validity. One important threat is low reliability of measures (see reliability). This can be due to many factors including poor question wording, bad instrument design or layout, illegibility of field notes, and so on. In studies where you are evaluating a program you can introduce noise through poor reliability of treatment implementation. If the program doesn't follow the prescribed procedures or is inconsistently carried out, it will be harder to see relationships between the program and other factors like the outcomes. Noise that is caused by random irrelevancies in the setting can also obscure your ability to see a relationship. In a classroom context, the traffic outside the room, disturbances in the hallway, and countless other irrelevant events can distract the researcher or the participants. The types of people you have in your study can also make it harder to see relationships. The threat here is due to random heterogeneity of respondents. If you have a very diverse group of resp

considered statistically significant. Nevertheless, even if there is only a slight probability that a difference is accidental, it can still be accidental. An accidental difference which is statistically significant

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is known formally as a Type I error, and less formally as a comprehensive error rate testing spurious difference. Spurious differences are not likely to be a huge problem if you're only testing one difference, but as bit error rate calculator the number of differences you're testing increases, the likelihood of detecting a spurious difference grows alarmingly, especially if you're using the most popular significance criterion. For example, if you're assessing ten differences with http://www.socialresearchmethods.net/kb/concthre.php a significance criterion of 5% (p < .05), you have a 40% chance of detecting at least one spurious difference. If you assess twenty differences, the probability is 64%. The problem is attenuated considerably if you use the 1% criterion I prefer, but it's still a problem. The equivalent probabilities are 10% in ten comparisons and 18% in twenty. One implication of this problem can be http://www.actualanalysis.com/erate.htm seen in the common practice of comparing opinion items individually. For example, people might be asked to rate their agreement with ten statements of opinion before they go into a program, and then to rate it again afterwards. If you compare the ratings of each individual item before and after and find one significant difference, you really cannot accept that as evidence of any change in opinion whatever. If you find two significant differences, you still have little reason to argue for a change in opinion. So what can you do about this problem? If all else fails you can always reduce the significance criterion, and the freeware will work out the value to which to reduce it. The best solution, though, is usually scaling. For example, the ten opinion items may all be intended to measure the same opinion, so scaling will allow you to work out a single attitude score for the ten items (as well as telling you whether you're justified in combining the ratings into a single score). We'll look at how to do that next week. The Error Rate Problem © 1999, John FitzGerald Home page | Decisionmakers' index | E-mail

Mora PUB. DATE December 1992 SOURCE Rehabilitation Counseling Bulletin;Dec92, Vol. 36 Issue 2, p66 SOURCE TYPE Academic Journal DOC. TYPE Article ABSTRACT Focuses on the fishing and error rate problem (FERP) which is also called http://connection.ebscohost.com/c/articles/9609040875/fishing-error-rate-problem alpha inflation. Increase in the probability of false positive findings; Causes of FERP; Occurrence when more than one statistical test is computed on the same data set; Avoidance of FERP by selecting a new sample for each statistical calculation; Recommendations for designing research. ACCESSION # 9609040875 Related ArticlesUSES AND ABUSES OF ANACHRONISM IN THE HISTORY OF THE SCIENCES.Jardine, Nick//History of Science;Sep2000, Vol. 38 Issue 3, error rate p251Discusses the interpretation of anachronism in science history. Presuppositions of interpretative anachronism; Disputes on the application of disciplinary categories; Issues related to the origins of biology.Learning from Our Errors.Azevedo, João Roberto D.; Andrioli, Mario Sergio//New England Journal of Medicine;3/20/97, Vol. 336 Issue 12, p876A letter to the editor is presented in response to the article "Learning From Our Errors," which was published in the October 3, 1996 error rate problem issue.Majority of hospitals won't bill for 'never events.'.//Healthcare Benchmarks & Quality Improvement;Jan2008, Vol. 15 Issue 1, p7The article reports that 52% of the hospitals responding to the Hospital Quality and Safety Survey of Leapfrog Group say that they have adopted the "Leapfrog Never Events" policy. The actions included in the policy that the hospitals pledge to take whenever medical errors occur are presented....Learning from Our Errors.Boardman, Hugh S.//New England Journal of Medicine;3/20/97, Vol. 336 Issue 12, p876A letter to the editor is presented in response to the article "Learning From Our Errors," which was published in the October 3, 1996 issue.Learning from Our Errors.Blank, Eugene//New England Journal of Medicine;3/20/97, Vol. 336 Issue 12, p876A letter to the editor is presented in response to the article "Learning From Our Errors," which was published in the October 3, 1996 issue.Learning from Our Errors.Brown, Jeremy; Worthington, Michael J.//New England Journal of Medicine;3/20/97, Vol. 336 Issue 12, p876A response by Jeremy Brown and Michael J. Worthington to a letter to the editor about their article "Learning From Our Errors," which was published in the October 3, 1996 issue, is presented.The consequence of errors.Weigmann, Katrin//EMBO Reports;Apr2005, Vol. 6 Issue 4, p306This article

 

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