ASTM, in standard E1847, defines replication as ".

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Replication: In statistics, replication is repetition of an experiment or observation in the same or similar conditions. Q3.

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Replicabillity — the ability to obtain the same result when an experiment is repeated — is foundational to science. While not stated explicitly in the Replication section, randomization is just as important as replication. Aug 25, 2022 · Reads 75.

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For example, if. replication is also important because it is used to measure variation in the experiment so that statistical tests can be applied to evaluate differences and increase the accuracy of. .

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It is important to understand the differences between repeat and replicate response measurements. 1.

When designing an experiment, replication allows for greater control over variables, which can produce. Replication increases the chances that your results apply only to specific cases.

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Aug 25, 2022 · Reads 75. Dec 15, 2021 · class=" fc-falcon">A high-profile replication study in cancer biology has obtained disappointing results. .

Thinking about our initial experiment where the field was split in two. . Another great use for replication is in confirming the results of subgroup comparisons. . .

While not stated explicitly in the Replication section, randomization is just as important as replication.

Replication is important because it adds information about the reliability of the conclusions or estimates to be drawn from the data. The only way to test a hypothesis is to perform an experiment.

The replication is so important in science.

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Sep 16, 2019 · Replication is not always easy to understand because many parts of an experiment can be replicated, and a non-exhaustive list includes: Replicating the measurements taken on a set of samples.

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