t-tests, non-parametric tests, and large studies--a paradox of statistical practice?
t-tests, non-parametric tests, and large studies--a paradox of statistical practice?
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DOI：
10.1186/1471-2288-12-78
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发表时间：
2012-06-14
影响因子：
4
通讯作者：
Fagerland MW
中科院分区：
文献类型：
--
作者：
Fagerland MW
在过去的30年里，在高影响力的医学期刊上发表的研究的中位数样本量增加了许多倍，而非参数检验的使用增加了t检验的费用。本文探讨了这种自相矛盾的做法，并说明其后果。一个模拟研究是用来比较拒绝率的Wilcoxon-Mann-Whitney（WMW）测试和两个样本的t-检验增加样本量。样本取自平均值和中位数相等但散布差异较小的偏态分布。一个假设的案例研究用于说明和动机。平均而言，WMW检验产生的p值小于t检验。这种差异随着样本量、偏度和分布差异的增加而增加。对于严重偏斜的数据，如果标准差相差10%并且每组中的观察值数量为1000，则WMW检验的p<0.05的比例可以大于90%。WMW测试的高拒绝率应解释为检测来自一个分布的随机样本小于来自另一个分布的随机样本的概率大于50%的功效。非参数检验对于小型研究最有用。在大型研究中使用非参数检验可能会提供错误问题的答案，从而使读者感到困惑。对于大样本量的研究，即使是严重偏斜的数据，也可以而且应该使用t检验及其相应的置信区间。
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. 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. 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%. 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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影响因子：
2
作者：
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影响因子：
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影响因子：
158.5
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