Replication (statistics)

Replication is not the same as repeated measurements of the same item. Both repeat and replicate measurements involve multiple observations taken at the same levels of experimental factors. However, repeat measurements are collected during a single experimental session, while replicate measurements are gathered across different experimental sessions. Replicates Example: Testing a new drug's effect on blood pressure in separate groups on different days. Repeats Example: Measuring blood pressure multiple times in one group during a single session. == Statistical methods in replication ==

In replication studies within the field of statistics, several key methods and concepts are employed to assess the reliability of research findings. Here are some of the main statistical methods and concepts used in replication: P-Values: The p-value is a measure of the probability that the observed data would occur by chance if the null hypothesis were true. In replication studies p-values help us determine whether the findings can be consistently replicated. A low p-value in a replication study indicates that the results are not likely due to random chance. For example, if a study found a statistically significant effect of a test condition on an outcome, and the replication find statistically significant effects as well, this suggests that the original finding is likely reproducible. Confidence Intervals: Confidence intervals provide a range of values within which the true effect size is likely to fall. In replication studies, comparing the confidence intervals of the original study and the replication can indicate whether the results are consistent. For example, if the original study reports a treatment effect with a 95% confidence interval of [5, 10], and the replication study finds a similar effect with a confidence interval of [6, 11], this overlap indicates consistent findings across both studies. ==Example==

As an example, consider a continuous process which produces items. Batches of items are then processed or treated. Finally, tests or measurements are conducted. Several options might be available to obtain ten test values. Some possibilities are: • One finished and treated item might be measured repeatedly to obtain ten test results. Only one item was measured so there is no replication. The repeated measurements help identify observational error. • Ten finished and treated items might be taken from a batch and each measured once. This is not full replication because the ten samples are not random and not representative of the continuous nor batch processing. • Five items are taken from the continuous process based on sound statistical sampling. These are processed in a batch and tested twice each. This includes replication of initial samples but does not allow for batch-to-batch variation in processing. The repeated tests on each provide some measure and control of testing error. • Five items are taken from the continuous process based on sound statistical sampling. These are processed in five different batches and tested twice each. This plan includes proper replication of initial samples and also includes batch-to-batch variation. The repeated tests on each provide some measure and control of testing error. • For proper sampling, a process or batch of products should be in reasonable statistical control; inherent random variation is present but variation due to assignable (special) causes is not. Evaluation or testing of a single item does not allow for item-to-item variation and may not represent the batch or process. Replication is needed to account for this variation among items and treatments. Each option would call for different data analysis methods and yield different conclusions. ==See also==