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Despite its importance, many studies fail reproducibility tests, leading to what is known as the replication crisis in fields like psychology, medicine, and social sciences. Some key challenges include: Insufficient Data Sharing – Many researchers do not make raw data, code, or methodology openly available, making replication difficult.
Focus on the replication crisis has led to renewed efforts in psychology to retest important findings. [41] [181] A 2013 special edition of the journal Social Psychology focused on replication studies. [13] Standardization as well as (requiring) transparency of the used statistical and experimental methods have been proposed. [182]
[3] [4] The results of the Reproducibility Project might also affect public trust in psychology. [9] [10] Lay people who learned about the low replication rate found in the Reproducibility Project subsequently reported a lower trust in psychology, compared to people who were told that a high number of the studies had replicated. [11] [9]
A replication attempt with a sample from a more diverse population, over 10 times larger than the original study, showed only half the effect of the original study. The replication suggested that economic background, rather than willpower, explained the other half. [6] [7] The predictive power of the marshmallow test was challenged in a 2020 study.
In engineering, science, and statistics, replication is the process of repeating a study or experiment under the same or similar conditions. It is a crucial step to test the original claim and confirm or reject the accuracy of results as well as for identifying and correcting the flaws in the original experiment. [1]
Because B does not appear to be important, it can be dropped from the model. Performing the ANOVA using factors A, C, and D, and the interaction terms A:C and A:D, gives the result shown in the following table, in which all the terms are significant (p-value < 0.05).
They are used to test theories and hypotheses about how physical processes work under particular conditions (e.g., whether a particular engineering process can produce a desired chemical compound). Typically, experiments in these fields focus on replication of identical procedures in hopes of producing identical results in each replication.
The use of a sequence of experiments, where the design of each may depend on the results of previous experiments, including the possible decision to stop experimenting, is within the scope of sequential analysis, a field that was pioneered [13] by Abraham Wald in the context of sequential tests of statistical hypotheses. [14]