8/11/2023 0 Comments Reflection definition biologyWe also remark on the assumptions that are made if a criterion is not met. Second, we provide detailed criteria for genuine replication that are applicable to all biological experiments and that researchers can use to design their experiments. We introduce instead the concept of biological units (BUs), EUs, and observational units (OUs), and argue that they can clarify where replication needs to occur. What do we hope to achieve with yet another discussion? First, we argue that the frequent and common distinction made between ‘biological’ and ‘technical’ replication is unhelpful because they are inconsistently defined, do not capture the important characteristics of an experiment, and do not clarify what to replicate. But, paraphrasing Goodman's comment on misinterpreting p-values, ‘these lessons appear to be either unread, ignored, not believed, or forgotten as each new wave of researchers is introduced to research’. Dunn discussed the distinction in 1929, and papers and books warning of the genuine versus pseudoreplication distinction have appeared regularly (with four papers and a book chapter in the past year). Sometimes it is less clear what aspect of an experiment should be replicated to increase the sample size, and far too often the wrong aspect is chosen. ![]() The wrong choice of replicate cannot be fixed by a clever statistical analysis after the experiment is completed (e.g., using multilevel or hierarchical models) it needs to be planned at the design stage. The multiple measurements on these 2 mice do not contribute to N and thus constitute pseudoreplication. Both designs provide 10 data points to calculate a p-value, but the p-value is meaningless for the first design because the hypothesis is about sex differences, and there is only 1 member of each sex. He could (1) weigh the brain of 1 male and 1 female mouse 5 times, or (2) weigh the brain of 5 male and 5 female mice once. An example will illustrate the difference: suppose a researcher hypothesises that male mice have heavier brains than female mice. Confusing pseudoreplication for genuine replication artificially inflates the sample size, thereby inflating the apparent evidence supporting a scientific claim, and contributes to irreproducible results. The second type is replication that does not increase the sample size and is called pseudoreplication. It is called true, genuine, or absolute replication, and when these qualifiers are not used, replication is understood to mean this type. The first is replication that increases the sample size ( N) and thus contributes to testing an experimental hypothesis. It does not refer to researchers trying to reproduce or replicate their own or others' results.īoth statisticians and biologists agree on the importance of replication and distinguish between two types. The term ‘replication’ has several related meanings, and here it refers to the classic statistical definition of an intervention or treatment applied to multiple biological entities (experimental units ). We focus here on replication-a critical part of experimental design that is often misunderstood, leading to poor design choices, which in turn contribute to irreproducible or meaningless results. We argue that distinguishing between biological units, experimental units, and observational units clarifies where replication should occur, describe the criteria for genuine replication, and provide concrete examples of in vitro, ex vivo, and in vivo experimental designs.ĭesigning experiments is challenging: there are many options to consider, decisions to make, and trade-offs to weigh, and a single poor design choice can make an experiment nearly worthless. Pseudoreplication artificially inflates the sample size, and thus the evidence for a scientific claim, resulting in false positives. Nearly half of the studies (46%, 95% CI = 38%–53%) had pseudoreplication while 32% (95% CI = 26%–39%) provided insufficient information to make a judgement. We found that only 22% of studies (95% CI = 17%–29%) replicated the correct entity–intervention pair and thus made valid statistical inferences. We surveyed a random sample of published animal experiments from 2011 to 2016 where interventions were applied to parents and effects examined in the offspring, as regulatory authorities provide clear guidelines on replication with such designs. If the wrong entity–intervention pair is chosen, an experiment cannot address the question of interest. When done appropriately, independent replication of the entity–intervention pair contributes to the sample size ( N) and forms the basis of statistical inference. ![]() ![]() Biologists determine experimental effects by perturbing biological entities or units.
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