magnitude-based inference
Hungarian translation: a hatások gyakorlati jelentőségét is meghatározó statisztikai következtetés
| 08:46 Mar 25, 2016 |
| English to Hungarian translations [PRO] Science - Sports / Fitness / Recreation / sports science | |||||||
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|  Selected response from: András Illyés Local time: 14:48 | ||||||
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| 3 | a hatások gyakorlati jelentőségét is meghatározó statisztikai következtetés |
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2 days 6 hrs confidence: 
a hatások gyakorlati jelentőségét is meghatározó statisztikai következtetés Explanation: Instead, we make an inference about the importance of an effect, based on the uncertainty in its magnitude. http://www.sportsci.org/2006/wghss.htm A konkrét cikkben szerintem így lehetne fordítani: Magnitude-based inferences were used to analyze the likelihood of an effect having a standardized (Cohen’s) effect size exceeding 0.20. Annak elemzésére, hogy egy hatásnak mekkora valószínűséggel lesz a 0,2-őt meghaladó sztenderdizált (Cohen-féle) hatásnagysága, a hatások jelentőségét is meghatározó következtetéseket használtunk. Olympic weightlifting was >80% likely to provide substantially better improvements than plyometric training for CMJ, horizontal jump - nem szignifikáns vagy nem szignifikáns eredmény, hanem 80%-nál nagyobb valószínűséggel jelentősen jobb eredmények érhetőek el a súlyemeléssel, mint a többi módszerrel substantially better - ez lenne a súlyemelés hatásának konkrét gyakorlati jelentősége valamekkora valószínűséggel, ahelyett hogy szignifikánsan jobb, vagy nem jobb-e valaminél. --- Nem találtam semmilyen magyar referenciát, ezért megkérdeznék egy statisztikust is, de nagyjából valami ilyesmiről van szó: A p-értéken alapuló statisztikai szignifikancia helyett alkalmazza ezt néhány statisztikus egy lehetséges alternatívaként, mivel a nullhipotézis tesztelést nem tartják alkalmasnak arra, hogy minőségileg is elemezzenek valamilyen hatást: Making Meaningful Inferences About Magnitudes Alan M Batterham, Will G Hopkins http://www.sportsci.org/jour/05/ambwgh.htm A study of a sample provides only an estimate of the true (population) value of an outcome statistic. A report of the study therefore usually includes an inference about the true value. Traditionally, a researcher makes an inference by declaring the value of the statistic statistically significant or non-significant on the basis of a p value derived from a null hypothesis test. This approach is confusing and can be misleading, depending on the magnitude of the statistic, error of measurement, and sample size. We use a more intuitive and practical approach based directly on uncertainty in the true value of the statistic. First we express the uncertainty as confidence limits, which define the likely range of the true value. We then deal with the real-world relevance of this uncertainty by taking into account values of the statistic that are substantial in some positive and negative sense, such as beneficial and harmful. If the likely range overlaps substantially positive and negative values, we infer that the outcome is unclear; otherwise, we infer that the true value has the magnitude of the observed value: substantially positive, trivial, or substantially negative. We refine this crude inference by stating qualitatively the likelihood that the true value will have the observed magnitude (e.g., very likely beneficial). Quantitative or qualitative probabilities that the true value has the other two magnitudes or more finely graded magnitudes (such as trivial, small, moderate, and large) can also be estimated to guide a decision about the utility of the outcome. A p-érték helyett meghatároznak egy konfidencia-intervallumot (90%), és a hatásokat ezen belül jellemzik, hátrányos, semleges, előnyös, különféle valószínűségekkel együtt (nagyon valószínű, valószínű, kevéssé valószínű, egyáltalán nem valószínű): To report inferences in a publication, I suggest we show 90% confidence intervals and the mechanistic inference for all effects but indicate also the clinical inference for those effects that have a direct application to health or performance. Ez a fajta kiértékelés látszik ezen a képen: http://www.sportsci.org/jour/05/ambwgh_files/image010.jpg Ebben a PPT-ben részletesen ki van fejtve, ábrákkal: MAGNITUDE-BASED INFERENCES: An alternative to hypothesis testing http://people.bath.ac.uk/jb335/Y2 Research Skills (FH200107)... For magnitude-based inferences, we interpret confidence limits in relation to the smallest clinically beneficial and harmful effects. Spreadsheets at sportsci.org provide the % likelihood that an effect is harmful | trivial | beneficial. Effects that cross thresholds for benefit and harm are classed as unclear. An effect should be almost certainly not harmful (<0.5%) and at least possibly beneficial (>25%) before you decide to use it. --- Van róla egy nagy review is, amiben erősen kritizálják a módszert: Magnitude-based Inference: A Statistical Review https://digitalcollections.anu.edu.au/bitstream/1885/13597/2... In response to such questions about significance testing, a number of researchers advocate the use of confidence intervals instead of P values (6,7,9,17,20) but magnitude-based inference tries to go further, replacing the confidence interval with probabilities that are supposedly based on the confidence interval. We show that magnitude-based inference is not a progressive improvement on modern statistics. The additional probabilities introduced are not directly related to the confidence interval but, rather, are interpretable either as P values for two different nonstandard tests (for different null hypotheses) or as approximate Bayesian calculations, which also lead to a type of test. |
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