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      Characterizing unsuccessful animal adoptions: age and breed predict the likelihood of return, reasons for return and post-return outcomes

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          Abstract

          A considerable number of adopted animals are returned to animal shelters post-adoption which can be stressful for both the animal and the owner. In this retrospective analysis of 23,932 animal records from a US shelter, we identified animal characteristics associated with the likelihood of return, key return reasons, and outcomes post-return for dogs and cats. Binary logistic regression models were used to describe the likelihood of return, return reason and outcome based on intake age, intake type, sex, breed and return frequency. Behavioral issues and incompatibility with existing pets were the most common return reasons. Age and breed group (dogs only) predicted the likelihood of return, return reason and post-adoption return outcome. Adult dogs had the greatest odds of post-adoption return (OR 3.40, 95% CI 2.88–4.01) and post-return euthanasia (OR 3.94, 95% CI 2.04–7.59). Toy and terrier breeds were 65% and 35% less likely to be returned compared with herding breeds. Pit bull-type breeds were more likely to be returned multiple times ( X 2  = 18.11, p = 0.01) and euthanized post-return (OR 2.60, 95% CI 1.47–4.61). Our findings highlight the importance of animal behavior in the retention of newly adopted animals and provide useful direction for allocation of resources and future adoption counselling and post-adoption support services.

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          No Adjustments Are Needed for Multiple Comparisons

          Adjustments for making multiple comparisons in large bodies of data are recommended to avoid rejecting the null hypothesis too readily. Unfortunately, reducing the type I error for null associations increases the type II error for those associations that are not null. The theoretical basis for advocating a routine adjustment for multiple comparisons is the "universal null hypothesis" that "chance" serves as the first-order explanation for observed phenomena. This hypothesis undermines the basic premises of empirical research, which holds that nature follows regular laws that may be studied through observations. A policy of not making adjustments for multiple comparisons is preferable because it will lead to fewer errors of interpretation when the data under evaluation are not random numbers but actual observations on nature. Furthermore, scientists should not be so reluctant to explore leads that may turn out to be wrong that they penalize themselves by missing possibly important findings.
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            Do multiple outcome measures require p-value adjustment?

            Background Readers may question the interpretation of findings in clinical trials when multiple outcome measures are used without adjustment of the p-value. This question arises because of the increased risk of Type I errors (findings of false "significance") when multiple simultaneous hypotheses are tested at set p-values. The primary aim of this study was to estimate the need to make appropriate p-value adjustments in clinical trials to compensate for a possible increased risk in committing Type I errors when multiple outcome measures are used. Discussion The classicists believe that the chance of finding at least one test statistically significant due to chance and incorrectly declaring a difference increases as the number of comparisons increases. The rationalists have the following objections to that theory: 1) P-value adjustments are calculated based on how many tests are to be considered, and that number has been defined arbitrarily and variably; 2) P-value adjustments reduce the chance of making type I errors, but they increase the chance of making type II errors or needing to increase the sample size. Summary Readers should balance a study's statistical significance with the magnitude of effect, the quality of the study and with findings from other studies. Researchers facing multiple outcome measures might want to either select a primary outcome measure or use a global assessment measure, rather than adjusting the p-value.
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              t-tests, non-parametric tests, and large studies—a paradox of statistical practice?

              Background During the last 30 years, the median sample size of research studies published in high-impact medical journals has increased manyfold, while the use of non-parametric tests has increased at the expense of t-tests. This paper explores this paradoxical practice and illustrates its consequences. Methods A simulation study is used to compare the rejection rates of the Wilcoxon-Mann-Whitney (WMW) test and the two-sample t-test for increasing sample size. Samples are drawn from skewed distributions with equal means and medians but with a small difference in spread. A hypothetical case study is used for illustration and motivation. Results The WMW test produces, on average, smaller p-values than the t-test. This discrepancy increases with increasing sample size, skewness, and difference in spread. For heavily skewed data, the proportion of p<0.05 with the WMW test can be greater than 90% if the standard deviations differ by 10% and the number of observations is 1000 in each group. The high rejection rates of the WMW test should be interpreted as the power to detect that the probability that a random sample from one of the distributions is less than a random sample from the other distribution is greater than 50%. Conclusions Non-parametric tests are most useful for small studies. Using non-parametric tests in large studies may provide answers to the wrong question, thus confusing readers. For studies with a large sample size, t-tests and their corresponding confidence intervals can and should be used even for heavily skewed data.
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                Author and article information

                Contributors
                lrpowell@upenn.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                13 April 2021
                13 April 2021
                2021
                : 11
                : 8018
                Affiliations
                [1 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, School of Veterinary Medicine, , University of Pennsylvania, ; Philadelphia, PA USA
                [2 ]Charleston Animal Society, North Charleston, SC USA
                Article
                87649
                10.1038/s41598-021-87649-2
                8044234
                33850258
                179e3cde-2dda-4130-88c9-f80666a26eec
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 January 2021
                : 30 March 2021
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                © The Author(s) 2021

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                animal behaviour,psychology
                Uncategorized
                animal behaviour, psychology

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