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Reconsidering Statistical Methods for Assessing Replication
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DOI：10.1037/met0000302.png)
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Abstract
En 中文
Recent empirical evaluations of replication in psychology have reported startlingly few successful replication attempts. At the same time, these programs have noted that the proper way to analyze replication studies is far from a settled matter and have analyzed their data in several different ways. This presents 2 challenges to interpreting the results of these programs. First, different analysis methods assess different operational definitions of replication. Second, the properties of these methods are not necessarily common knowledge; it is possible for a successful replication to be deemed a failure by nearly all of the metrics used, and it is not always immediately clear how likely such errors are to occur. In this article, we describe the methods commonly used in replication research and how they imply specific operational definitions of replication. We then compute the probability of false failure (i.e., a successful replication is concluded to have failed) and false success determinations. These are shown to be high (often over 50%) and in many cases uncontrolled. We then demonstrate that errors are probable in the data to which these methods have been applied in the literature. We show that the probability that some reported conclusions about replication are incorrect can be as high as 75-80%. Translational Abstract This article examines analysis methods for studying replications, and it finds that some of the commonly used methods have severe limitations. These limitations include the fact that a replication failure could arise by chance alone with a surprisingly high probability, even if the studies successfully replicate.
Keywords:
replication
error
meta-analysis
hypothesis testing