Compared with optimal NHST, the SBF design typically needs 50% to 70% smaller samples to reach a conclusion about the presence of an effect, while having the same or lower long-term rate of wrong inference. In addition to the usual specifications, it provides the total number of stages (the number of interim stages plus a final stage) and a stopping criterion to reject, accept, or either reject or accept the null hypothesis at each interim stage. We investigated the long-term rate of misleading evidence, the average expected sample sizes, and the biasedness of effect size estimates when an SBF design is applied to a test of mean differences between 2 groups. SAS/STAT group sequential design provides detailed specifications for a group sequential trial. Sequential Analysis is different from Classical Hypothesis Testing were the number of cases tested or collected is fixed at the beginning of the experiment. Sequential testing employs optional stopping rules (error-spending functions) that guarantee the overall type I error rate of the procedure. Sequential testing is the practice of making decision during an A/B test by sequentially monitoring the data as it accrues. This allows flexible sampling plans and is not dependent upon correct effect size guesses in an a priori power analysis. What is Sequential Testing Aliases: sequential monitoring, group-sequential design, GSD, GST. In this procedure, which we call Sequential Bayes Factors (SBFs), Bayes factors are computed until an a priori defined level of evidence is reached. In this contribution, we investigate the properties of a procedure for Bayesian hypothesis testing that allows optional stopping with unlimited multiple testing, even after each participant. Despite these criticisms, this research practice is not uncommon, probably because it appeals to researcher's intuition to collect more data to push an indecisive result into a decisive region. A t-statistic is calculated from the raw mean difference and sample.
Unplanned optional stopping rules have been criticized for inflating Type I error rates under the null hypothesis significance testing (NHST) paradigm. To begin the group-sequential testing process, an initial calculation should be made.
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