Search results
Results from the WOW.Com Content Network
Units are often referred to as being either experimental units or sampling units: An "experimental unit" is typically thought of as one member of a set of objects that are initially equal, with each object then subjected to one of several experimental treatments. Put simply, it is the smallest entity to which a treatment is applied.
In the statistical theory of the design of experiments, blocking is the arranging of experimental units in groups (blocks) that are similar to one another. Typically, a blocking factor is a source of variability that is not of primary interest to the experimenter. [3] [4] No blocking (left) vs blocking (right) experimental design
However, repeat measurements are collected during a single experimental session, while replicate measurements are gathered across different experimental sessions. [2] Replication in statistics evaluates the consistency of experiment results across different trials to ensure external validity, while repetition measures precision and internal ...
Inferential statistics cannot separate variability due to treatment from variability due to experimental units when there is only one measurement per unit. Temporal pseudoreplication (Figure 5c in Hurlbert 1984) occurs when experimental units differ enough in time that temporal effects among units are likely, and treatment effects are ...
Blocking (right) Blocking is the non-random arrangement of experimental units into groups (blocks) consisting of units that are similar to one another. Blocking reduces known but irrelevant sources of variation between units and thus allows greater precision in the estimation of the source of variation under study.
Tukey's F-statistic for testing interaction has a distribution based on the randomized assignment of treatments to experimental units. When Mandel's multiplicative model holds, the F-statistics randomization distribution is closely approximated by the distribution of the F-statistic assuming a normal distribution for the error, according to the ...
Designed experiments with full factorial design (left), response surface with second-degree polynomial (right) In statistics, a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors.
Randomization is a statistical process in which a random mechanism is employed to select a sample from a population or assign subjects to different groups. [1] [2] [3] The process is crucial in ensuring the random allocation of experimental units or treatment protocols, thereby minimizing selection bias and enhancing the statistical validity. [4]