![]() ![]() Statistical methods in psychology journals: Guidelines and explanations. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. Measuring the prevalence of Questionable Research Practices with incentives for truth telling. In such contexts, interim analysis is an appealing alternative. In practice, however, it is not always feasible to employ this approach, for instance when participants need to undergo group therapy in groups of size 20. This is the same with the full sequential method, but here one can also stop when it is sufficiently clear that H 0 will not be rejected. In interim analysis, one can stop data collection early in case there is sufficient evidence to reject H 0. Statistically, this is the optimal approach of deciding upon the sample size. The computation of this log-likelihood ratio is far from straightforward. Wald’s procedure, for instance, involves computing the cumulative log-likelihood ratio after each observation, and stopping when this sum leaves a pre-specified interval ( a, b). These sequential approaches are more technical than standard methods. ![]() Theories about this by Abraham Wald 14 and Alan Turing 19, 20 date back to the 1940s. In full sequential approaches, one doesn’t check the data at a few pre-specified points, but after every observation. The Bonferroni-correction, and other corrections, ensure that this so-called familywise error rate remains at an acceptable level. When, for instance, performing 10 independent tests, whilst H 0 is true, then the probability of finding at least one false positive is equal to 1 – (1 − 0.05) 10 = 40.13%, very high. This 5% is something many scientists think is an acceptably small probability for incorrectly rejecting the null hypothesis (although you can make a motivated choice for another rate 8, 9). If the null hypothesis holds true, a single statistical test will yield a false positive, so p < 0.05, in 5% of the times. Corrections such as the Bonferroni-correction are included in most statistical textbooks. In this scenario, due to a large number of statistical tests being performed, the number of false-positives is increased and this needs to be corrected for (Fig. Let us create a complete end to end neural network model using Keras Sequential Model in this example.The problem with multiple statistical testing is more often recognized in the context of multiple independent testing. You have complete control and flexibility but beware you need to be really good at it and should be used by advanced users only. Model Subclassing is useful in those scenarios when you are researching and would like to create all aspects of the neural network from scratch.This allows you to design advanced neural networks for complex problems but will require some learning curve as well. Keras Functional API addresses the above shortcomings by giving you the flexibility to design complex topologies of neural network which includes shared layers, branching, and multiple input and output.With this restriction, you may not be able to create models with high accuracy for complex problems. ![]() It is useful for beginners for simple use but you cannot create advanced architectures.
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