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In engineering, science, and statistics, replication is the process of repeating a study or experiment under the same or similar conditions to support the original claim, which is crucial to confirm the accuracy of results as well as for identifying and correcting the flaws in the original experiment. [1]
Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method.For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated.
Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. [1] For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed.
Fay's method is a generalization of BRR. Instead of simply taking half-size samples, we use the full sample every time but with unequal weighting: k for units outside the half-sample and 2 − k for units inside it. (BRR is the case k = 0.)
This method uses Gaussian process regression (GPR) to fit a probabilistic model from which replicates may then be drawn. GPR is a Bayesian non-linear regression method. A Gaussian process (GP) is a collection of random variables, any finite number of which have a joint Gaussian (normal) distribution.
Sample size determination or estimation is the act of choosing the number of observations or replicates to include in a statistical sample.The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
In contrast to conventional methods, the interaction among process variables can be determined by statistical techniques. For instance, in a study, a central composite design was employed to investigate the effect of critical parameters of organosolv pretreatment of rice straw including temperature, time, and ethanol concentration.