How many replications on treatments must be done?
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The number of replications required for experiments varies depending on the specific context. In the case of maize hybrid competition experiments, it was found that at least 11 replications were needed to ensure a minimum significant level of 95% for grain yield . In the context of ecosystem experiments, substantial and sustained manipulations using multiple replicates produced significant results in the majority of simulated experiments . However, due to limitations such as high costs and scarcity of experimental ecosystems, it may not always be feasible to have an adequate number of replicates . In the field of psychotherapy clinical trials, there is a lack of direct replication attempts for trials with large effects, raising questions about the replicability of treatment superiority conclusions . The number of replicates required for studies on sperm production varies among species and end points, with some requiring a larger number of replicates than typically used in investigations .
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| Papers (4) | Insight |
|---|---|
| The paper discusses the importance of replication in experiments but does not provide a specific number of replications required for treatments. | |
| The paper states that for whole-lake experiments on food web manipulations, at least 10 replicate lakes are needed, and manipulations should enhance piscivore biomass at least 5-10 times for several years to have a reasonably high probability of detecting responses in primary production. | |
| The paper does not explicitly mention the exact number of replications required for treatments. However, it suggests using designs with three or four replicates and block sizes not exceeding 15 or 20. | |
Open access•Journal Article | The paper suggests that at least 11 replications should be done in maize hybrid competition experiments to ensure a minimum significant level of 95% for grain yield. |
Related Questions
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Why is the replication of experimental important in biosciences?4 answersReplication of experiments is important in biosciences for several reasons. Firstly, it helps to address the issue of irreproducibility, which is prevalent in the field. Many published results are found to be irreproducible, and replication studies can help identify the factors that contribute to this problem. Secondly, replication allows for the validation of findings and ensures the robustness of scientific knowledge. By replicating experiments, researchers can verify the accuracy and reliability of previous results, which is crucial for building upon existing knowledge and making informed decisions for further research. Additionally, replication studies can help identify any potential biases or errors in the original experiments, leading to improvements in experimental design and methodology. Overall, replication of experiments in biosciences is essential for ensuring the credibility and advancement of scientific research.
What are the seven replication strategies?4 answersReplication strategies mentioned in the abstracts include correcting perceived shortcomings in the original study, investigating the generality of previous results, resolving inconsistencies with later results or theories, testing the original theory, and replicating the theoretical meanings of the original tasks. Replication strategy is also discussed as a process involving exploration to develop the best business model and exploitation through large-scale replication. In the context of data grid systems, dynamic data replication strategies are studied and classified based on the target data grid architecture. The replication of Dhaka's community-based decentralized composting (DCDC) model in other developing cities of Asia requires strategies such as product quality control, partnership among stakeholders, involvement of the private sector, simple and low-cost technology, and effective interface among sectors. The evolution and maintenance of sexual replication as the preferred strategy in complex, multicellular organisms is explored through three replication models.
Why are biological and technical replicates are important in experiments?5 answersBiological and technical replicates are important in experiments because they help to assess and isolate sources of variation in measurements, improve the accuracy and reliability of summary statistics, and limit the impact of measurement error and spurious variation on hypothesis testing and parameter estimation. Biological replicates involve parallel measurements of biologically distinct samples, capturing random biological variation, while technical replicates are repeated measurements of the same sample, representing independent measures of random noise associated with protocols or equipment. By averaging technical replicates, the impact of measurement error can be reduced, but additional biological replicates are often preferred for improving the efficiency of statistical testing. Increasing the number of biological replicates increases the reliability of peak identification in ChIP-seq experiments, and a simple majority rule can identify peaks more reliably than the absolute concordance of peak identification between any two replicates.
How many replicates is okay for microbiological analysis?4 answersThe number of replicates required for microbiological analysis varies depending on the specific study. Some studies suggest that eight replicate sample units are needed to ensure adequate analytical precision. However, other studies have shown that replicate plating from a common source is no more accurate than single plating. It is generally recommended to perform single analyses on as many samples as can be afforded, rather than duplicating or triplicating laboratory analyses on a smaller number of samples. Additionally, the level of sequencing effort used for analyzing microbial communities can bias the results, and it is important to have sufficient sequencing coverage to accurately assess microbial diversity and differences between environments. For high-throughput expression datasets, it is proposed that at least six biological replicates per condition should be used to avoid false negatives, false positives, irreproducibility, and failure to detect real biological signatures.