How can you determine the appropriate replication level for your experiment?

One of the key decisions you need to make when designing an experiment is how many times you will repeat each treatment or condition. This is called the replication level, and it affects the validity, accuracy, and power of your results. In this article, you will learn how to determine the appropriate replication level for your experiment based on your research question, resources, and statistical methods.

1 Why replicate?

Replication is the process of repeating an experiment under the same or similar conditions to reduce the effects of random errors and variability. By replicating your experiment, you can increase the confidence in your findings, test the consistency of your results, and estimate the variability of your response variable. Replication also allows you to apply inferential statistics to compare the effects of different treatments or factors on your outcome of interest.

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Ravi Dashnamoorthy, Ph.D

Cancer Biologist, my opinions are entirely personal.
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Replication is a crucial aspect of scientific research. The complexity of biological systems, the difficulty of predicting their trajectories, and the sheer number of variables make them quite different from other fields in science. Therefore, any conclusions drawn from biological research must be reproducible. This not only adds credibility to our work, but also gives us self-confidence. Additionally, every discovery in science lays the foundation for the next discovery, and reproducibility ensures that the foundation is rock solid.

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2 How many replicates?

The number of replicates you need for your experiment is dependent on several factors, such as the type and complexity of the experiment, the magnitude and variability of the effect you are measuring, the level of confidence and precision you want to achieve, and the resources and constraints available. For instance, if you are conducting a simple experiment with one factor and two levels, you may need fewer replicates than if you are conducting a multifactorial experiment with interactions and covariates. Similarly, if you are expecting a large and consistent effect, you may need fewer replicates than if you are expecting a small and variable effect. Moreover, if you want to have a high probability of detecting a significant difference between treatments, more replicates may be needed than if you are willing to accept a lower level of certainty. Finally, if there are limited time, budget, or materials available, you may need to compromise on the number of replicates that can be performed.

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Ravi Dashnamoorthy, Ph.D

Cancer Biologist, my opinions are entirely personal.
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Three replicates and three independent experiments are sufficient. Graded variables (e.g., dose effects), kinetics, and time effects, should be incorporated into the narrative. Even though reviewers demand replicates, the ideal number depends on the researcher's skill and confidence in confirming their hypothesis. For every research question, I have historically adopted two independent methodologies/experimental approaches. There should be no doubt if adapting multiple methods provides the same conclusions.

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3 How to estimate replicates?

One way to estimate the number of replicates you need for your experiment is to use a power analysis. A power analysis is a statistical tool that calculates the probability of detecting a significant effect given a certain sample size, effect size, and significance level. A power analysis can help you determine the minimum number of replicates you need to achieve a desired level of power, which is usually set at 80% or higher. Alternatively, you can use a sample size calculator or a formula to estimate the number of replicates based on the expected standard deviation, mean difference, and significance level of your experiment.

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Ravi Dashnamoorthy, Ph.D

Cancer Biologist, my opinions are entirely personal.
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Replication estimates are context-dependent. A larger sample size is needed when the population is heterogeneous or the conditions of the experiment are genetically divergent in order to derive biologically meaningful conclusions. However, with a carefully controlled laboratory-based experimental system, a few replicates and independent evaluation would be sufficient.

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4 How to allocate replicates?

Once you have determined the number of replicates for your experiment, you need to decide how to allocate them among the treatments or conditions you are testing. Randomization is one way to do this, which involves assigning replicates randomly without any order or pattern to avoid systematic biases and confounding factors. Blocking is another option, which involves grouping replicates into blocks that share characteristics or conditions that may influence the response variable. Balancing is a third method, where you ensure that each treatment has the same number of replicates or a proportional number of replicates depending on the design of your experiment. All three of these methods can help increase precision and reduce error variance in your analysis.

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5 How to report replicates?

When you report the results of your experiment, you need to specify the number and allocation of replicates you used, as well as the rationale and method for choosing them. You also need to provide the descriptive and inferential statistics that summarize your data and test your hypotheses, such as the mean, standard deviation, confidence interval, and p-value. You can use tables, graphs, or text to present your results, depending on the format and style of your report. You should also discuss the implications and limitations of your replication level, and suggest any improvements or modifications for future experiments.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Ravi Dashnamoorthy, Ph.D

Cancer Biologist, my opinions are entirely personal.
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Replication validated by statistics is not the only reason why replication is necessary. A biological system can defy statistics, as it is not contained by mathematical rules. Don't forget that a cell divides to multiply, not to decimate. Reproducibility ensures that there is no error in the experiment, whether it is done by you or someone else on your behalf. Even junk studies can provide statistically significant reproducible results for highly skilled individuals. Thus, reproducibility serves two purposes: first, setting a high standard for yourself, and second, having confidence in your work.

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How can you determine the appropriate replication level for your experiment?

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1 Why replicate?

2 How many replicates?

3 How to estimate replicates?

4 How to allocate replicates?

5 How to report replicates?

6 Here’s what else to consider

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