The Experts below are selected from a list of 18051 Experts worldwide ranked by ideXlab platform
Markus Neuhäuser - One of the best experts on this subject based on the ideXlab platform.
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When should we use One‐Tailed hypothesis Testing?
Methods in Ecology and Evolution, 2010Co-Authors: Graeme D. Ruxton, Markus NeuhäuserAbstract:Summary 1. Although One-Tailed hypothesis Tests are commonly used, clear justification for why this approach is used is often missing from published papers. 2. Here we suggest explicit questions authors should ask of themselves when deciding whether or not to adopt One-Tailed Tests. 3. First, we suggest that authors should only use a One-Tailed Test if they can explain why they are more interested in an effect in One direction and not the other. 4. We suggest a further requirement that adoption of One-Tailed Testing requires an explanation why the authors would treat a large observed difference in the unexpected direction no differently from a difference in the expected direction that was not strong enough to justify rejection of the null hypothesis. 5. These justifications should be included in published works that use One-Tailed Tests, allowing editors, reviewers and readers the ability to evaluate the appropriateness of the adoption of One-Tailed Testing. 6. We feel that adherence to our suggestions will allow authors to use One-Tailed Tests more appropriately, and readers to form their own opinion about such appropriateness when One-Tailed Tests are used.
Graeme D. Ruxton - One of the best experts on this subject based on the ideXlab platform.
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When should we use One‐Tailed hypothesis Testing?
Methods in Ecology and Evolution, 2010Co-Authors: Graeme D. Ruxton, Markus NeuhäuserAbstract:Summary 1. Although One-Tailed hypothesis Tests are commonly used, clear justification for why this approach is used is often missing from published papers. 2. Here we suggest explicit questions authors should ask of themselves when deciding whether or not to adopt One-Tailed Tests. 3. First, we suggest that authors should only use a One-Tailed Test if they can explain why they are more interested in an effect in One direction and not the other. 4. We suggest a further requirement that adoption of One-Tailed Testing requires an explanation why the authors would treat a large observed difference in the unexpected direction no differently from a difference in the expected direction that was not strong enough to justify rejection of the null hypothesis. 5. These justifications should be included in published works that use One-Tailed Tests, allowing editors, reviewers and readers the ability to evaluate the appropriateness of the adoption of One-Tailed Testing. 6. We feel that adherence to our suggestions will allow authors to use One-Tailed Tests more appropriately, and readers to form their own opinion about such appropriateness when One-Tailed Tests are used.
Charan Singh Rayat - One of the best experts on this subject based on the ideXlab platform.
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Tests of Significance
Statistical Methods in Medical Research, 2018Co-Authors: Charan Singh RayatAbstract:Statistician applies appropriate “statistical Test” to analyze the given data in order to draw conclusion. Such a conclusion or inference is called “statistical decision.” ConTesting the null hypothesis (Ho) without any bias is essential. Accepting or rejecting the Ho illogically would lead to errOneous decisions. One-Tailed or two-Tailed Tests are generally applied depending on the scope and requirement of given data. One-Tailed Test is applied for Testing the hypothesis that One process is better than the other. These Tests are applied to two sets of observations from the same subjects or two sets of observations for the same parameter from two different groups of subjects. Student’s t-distribution plays a great role in sampling. When sample size is large (n > 30), the sampling distribution is considered normal. But, when the sample size is small (n < 30), the sampling distribution may not be normal. This chapter is focused on the applications of student’s t-Test for statistical decisions.
Stig Larsen - One of the best experts on this subject based on the ideXlab platform.
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Thiazide Prophylaxis of Urolithiasis
Acta Medica Scandinavica, 2009Co-Authors: Even Lærum, Stig LarsenAbstract:Fifty recurrent stOne formers were included in a double-blind randomized study (median 3 years) performed in a Norwegian general practice to compare twice daily administration of 25 mg hydrochlorothiazide versus placebo. The number of patients with new stOnes was significantly higher in the placebo group than in the thiazide group (p=0.05, One-Tailed Test). If a new stOne was formed, thiazide, but not placebo, had the effect of prolonging the stOne-free interval (p≤0.01). The probability of not forming a new stOne during the treatment period was 45% for the placebo group and 75% for the thiazide group. The thiazide effect seemed to be independent of urinary calcium, but was less beneficial in patients with hyperuricosuria. The placebo group also showed a substantial decrease in the expected number of new stOnes (p≤0.01), emphasizing the importance of an adequate control group.
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thiazide prophylaxis of urolithiasis a double blind study in general practice
Acta Medica Scandinavica, 2009Co-Authors: Even Lærum, Stig LarsenAbstract:Fifty recurrent stOne formers were included in a double-blind randomized study (median 3 years) performed in a Norwegian general practice to compare twice daily administration of 25 mg hydrochlorothiazide versus placebo. The number of patients with new stOnes was significantly higher in the placebo group than in the thiazide group (p = 0.05, One-Tailed Test). If a new stOne was formed, thiazide, but not placebo, had the effect of prolonging the stOne-free interval (p less than or equal to 0.01). The probability of not forming a new stOne during the treatment period was 45% for the placebo group and 75% for the thiazide group. The thiazide effect seemed to be independent of urinary calcium, but was less beneficial in patients with hyperuricosuria. The placebo group also showed a substantial decrease in the expected number of new stOnes (p less than or equal to 0.01), emphasizing the importance of an adequate control group.
Xiaolin Zhuo - One of the best experts on this subject based on the ideXlab platform.
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interpreting t statistics under publication bias rough rules of thumb
Journal of Quantitative Criminology, 2018Co-Authors: Christopher Winship, Xiaolin ZhuoAbstract:A key issue is how to interpret t-statistics when publication bias is present. In this paper we propose a set of rough rules of thumb to assist readers to interpret t-values in published results under publication bias. Unlike most previous methods that utilize collections of studies, our approach evaluates the strength of evidence under publication bias when there is only a single study. We first re-interpret t-statistics in a One-Tailed hypothesis Test in terms of their associated p-values when there is extreme publication bias, that is, when no null findings are published. We then consider the consequences of different degrees of publication bias. We show that under even moderate levels of publication bias adjusting One’s p-values to insure Type I error rates of either 0.05 or 0.01 result in far higher t-values than those in a conventional t-statistics table. Under a conservative assumption that publication bias occurs 20 percent of the time, with a One-Tailed Test at a significance level of 0.05, a t-value equal or greater than 2.311 is needed. For a two-Tailed Test the appropriate standard would be equal or above 2.766. Both cutoffs are far higher than the traditional Ones of 1.645 and 1.96. To achieve a p-value less than 0.01, the adjusted t-values would be 2.865 (One-tail) and 3.254 (two-tail), as opposed to the traditional values 2.326 (One-tail) and 2.576 (two-tail). We illustrate our approach by applying it to evaluate the hypothesis Tests in recent issues of Criminology and Journal of Quantitative Criminology (JQC). Under publication bias much higher t-values are needed to restore the intended p-value. By comparing the observed Test scores with the adjusted critical values, this paper provides a rough rule of thumb for readers to evaluate the degree to which a reported positive result in a single publication reflects a true positive effect. Further measures to increase the reporting of robust null findings are needed to ameliorate the issue of publication bias.
