Statistical Power Analysis for Animal Studies

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-22

Key Takeaways

Power analysis quantifies the minimum sample size required to detect a biologically meaningful effect, preventing wasted animal resources and missed discoveries by calculating the probability of rejecting the null hypothesis when the alternative is true.
Key inputs for power analysis include the effect size (magnitude of the biological difference), standard deviation (variability of measurements), significance level (alpha, typically 0.05 for false positives), and desired power (typically 0.80 for detecting true effects).
The experimental unit, not individual animals, dictates sample size; for instance, if a treatment is applied to a cage, the cage is the unit, and animals within are subsamples.
Effect size estimation is critical and should be based on pilot data, literature review, or meta-analyses, focusing on the smallest biologically meaningful difference rather than an optimistic expectation.
Common designs like two-group comparisons (t-tests) and multiple groups (ANOVA) require specific inputs, with repeated-measures designs leveraging within-subject correlation to increase statistical power.
Post-hoc power calculations using observed effect sizes are invalid; power analysis must be performed prospectively to guide study design and justify animal numbers to ethical review boards (IACUCs) and funding agencies.

Quick Answer

Power analysis estimates the minimum sample size needed to detect a true biological effect, preventing wasted animals and missed findings in animal studies.
Calculate power before data collection using an expected effect size, significance level (alpha), and desired power (typically 0.80), then adjust the design if the required sample size is not feasible.
Power depends on the true effect size, which is often uncertain in animal research, so use conservative estimates from pilot data or published literature and report the assumptions clearly.

At a Glance

Design	Typical Test	Key Inputs	Common Output	Practical Consideration
Two-group comparison	Independent t-test	Effect size, alpha, power	Sample size per group	Unequal group sizes reduce efficiency, aim for balanced groups
Multiple groups	One-way ANOVA	Effect size, alpha, power, number of groups	Total sample size	Post-hoc comparisons require larger samples than omnibus test
Repeated measures	Mixed-effects model	Effect size, correlation, alpha, power	Total sample size	Within-subject correlation strongly influences required sample size
Pilot study	Descriptive statistics	Precision, confidence level	Sample size for estimation	Use confidence intervals, not hypothesis tests, for pilot aims

Why Sample Size Determines Whether an Animal Study Can Answer Its Question

Animal studies consume substantial resources in housing, husbandry, veterinary oversight, and investigator time. When a study is underpowered, it cannot reliably detect the biological effect it was designed to measure, even when that effect is real. The result is a wasted experiment that may mislead subsequent research and fail to justify the use of animals. Power analysis is the quantitative tool that aligns the number of animals with the scientific question, the expected variability, and the effect size of interest.

The core relationship is straightforward. Statistical power is the probability that a test will reject the null hypothesis when the alternative hypothesis is true. In practical terms, power is the chance that the study will find a statistically significant difference when a true difference exists. Power depends on four factors: the significance level (alpha), the sample size, the variability of the measurements, and the magnitude of the effect being studied. The researcher controls the sample size and the alpha level, can reduce variability through careful experimental design, and must estimate the effect size from prior knowledge or pilot data.

The significance level, alpha, is the probability of a false positive, concluding that an effect exists when it does not. The convention in most life-science research is alpha equal to 0.05, meaning a 5 percent chance of a false positive. The desired power is conventionally set at 0.80, meaning an 80 percent chance of detecting a true effect. These values are not fixed by any universal rule. They are choices that reflect the consequences of false positives and false negatives in the specific research context. A study that screens many candidate treatments may accept lower power to reduce the number of animals used, while a confirmatory study that will guide a major decision may require higher power.

The sample size is the number of independent experimental units per group. In animal research, the experimental unit is not always the individual animal. If animals are housed together and the treatment is applied to the cage, the cage is the experimental unit. Confusing the animal with the experimental unit is a common error that inflates the apparent sample size and produces overconfident conclusions. The power analysis must be based on the number of independent units, not the number of individual measurements.

The variability of the measurements is captured by the standard deviation. Lower variability means that a smaller sample size can detect a given effect. Variability can be reduced by standardizing the experimental conditions, using inbred strains where appropriate, controlling environmental factors, and using precise measurement instruments. However, reducing variability too aggressively can limit the generalizability of the findings, because the controlled conditions may not reflect the broader population of interest.

The effect size is the magnitude of the difference that the study is designed to detect. It is the most difficult input to specify because it requires a scientific judgment about what difference is biologically meaningful. A study that is powered to detect a very small effect will require a large sample size. A study that is powered to detect only a large effect will require fewer animals but may miss a smaller effect that is still biologically important. The choice of effect size should be justified by the research question, not by convenience or by the number of animals that are available.

The practical consequence of underpowering is that a study may fail to detect a real effect, leading to a false negative. The scientific literature is biased toward positive results, so a false negative may never be published, and the true effect may remain unknown. The animals used in the study have been used without producing reliable knowledge. This is an ethical and scientific failure that can be prevented by careful power analysis before the study begins.

Core Principles of Power Analysis for Animal Studies

The Four Inputs and One Output

Power analysis requires four inputs and produces one output. The four inputs are the effect size, the standard deviation, the significance level, and the desired power. The output is the required sample size. In some cases, the sample size is fixed by practical constraints, and the power analysis is used to calculate the power that the study will have. In other cases, the effect size is the unknown, and the analysis is used to determine the minimum effect that the study can detect with a given sample size.

The relationship among these quantities is deterministic. Given any three of the four inputs, the fourth can be calculated. The researcher must specify the effect size, the standard deviation, the alpha level, and the desired power to obtain the sample size. If the sample size is fixed, the researcher can specify the effect size, the standard deviation, and the alpha level to obtain the power. This flexibility is useful when the number of animals is constrained by ethical, financial, or logistical considerations.

The standard deviation is often estimated from previous studies, pilot data, or published values for the same or similar measurements. The estimate should be conservative, meaning that it should be slightly larger than the expected value, because an underestimate of the variability will lead to an underestimate of the required sample size. If the true variability is larger than the estimate, the study will be underpowered.

The effect size is the difference between the groups that the study is designed to detect. In a two-group comparison, the effect size can be expressed as the difference between the group means divided by the standard deviation. This standardized measure is called Cohen's d. A value of 0.2 is considered small, 0.5 is considered medium, and 0.8 is considered large. These labels are only conventions and should not replace a scientific judgment about the effect that matters for the research question.

The Experimental Unit and Replication

The experimental unit is the smallest unit to which a treatment is independently applied. In animal studies, the experimental unit is often the animal, but it can be the litter, the cage, or the pen. The number of experimental units determines the sample size for the power analysis. If the treatment is applied to the cage, the cage is the unit, and the number of animals per cage is a form of subsampling that does not increase the effective sample size.

Replication is the number of independent experimental units per group. It is the basis for the estimate of the variability and the power of the test. Pseudoreplication occurs when the analysis treats subsamples as independent units, inflating the sample size and the power. This is a common error in animal studies where multiple measurements are taken from the same animal or from animals in the same cage. The power analysis must be based on the number of independent units, not the number of measurements.

The distinction between the experimental unit and the measurement unit is critical for the design of the study and the interpretation of the power analysis. If the unit is the cage, the sample size is the number of cages, not the number of animals. The number of animals per cage can be increased to improve the precision of the cage-level measurement, but it does not increase the power of the test. The power analysis should be conducted at the level of the experimental unit.

The Role of the Effect Size

The effect size is the scientific input that requires the most judgment. The researcher must specify the smallest effect that is biologically meaningful for the research question. This is not the effect that the researcher hopes to find. It is the effect that would change the interpretation of the results or the decision that the study is designed to inform.

The effect size can be estimated from the literature, from pilot data, or from a meta-analysis of previous studies. The estimate should be based on the specific measurement and the specific comparison that the study will make. A general effect size from a different species or a different measurement may not be applicable. The researcher should also consider the direction of the effect, because a one-sided test requires a smaller sample size than a two-sided test, but it is only appropriate when the direction is known in advance.

The choice of the effect size is a scientific decision that should be documented in the study protocol. The documentation should include the source of the estimate, the reasoning behind the choice, and the sensitivity of the sample size to the effect size. A sensitivity analysis that shows the sample size for a range of effect sizes is useful for the review of the study design and for the interpretation of the results.

The Power Analysis Workflow

Step 1: Define the Research Question and the Primary Outcome

The first step is to define the research question in terms of a specific comparison and a specific outcome. The outcome should be the primary endpoint that will be used to answer the question. The comparison should be the contrast between the groups that will be tested. The research question should be stated in advance, because the power analysis depends on the specific test that will be used.

The primary outcome should be a single variable that is measured on every experimental unit. The outcome should be reliable, valid, and responsive to the treatment. The outcome should also be the variable that is most important for the research question. If the study has multiple outcomes, the power analysis should be based on the primary outcome, and the analysis of the secondary outcomes should be interpreted with caution.

The comparison should be defined in terms of the groups and the direction of the effect. The comparison can be a difference between two groups, a difference among several groups, or a trend across a range of doses. The comparison should be specified in advance, and the power analysis should be based on the test that will be used for that comparison.

Step 2: Select the Statistical Test

The choice of the statistical test depends on the design of the study and the nature of the outcome. The most common tests for animal studies are the t-test for two groups and the ANOVA for more than two groups. The t-test is used when the outcome is continuous and the groups are independent. The ANOVA is used when the outcome is continuous and the groups are more than two. The ANOVA tests the overall difference among the groups, and the post-hoc tests are used to compare the individual groups.

The choice of the test also depends on the distribution of the outcome. The t-test and the ANOVA assume that the outcome is normally distributed within each group. If the outcome is not normally distributed, the data may need to be transformed, or a nonparametric test may be used. The power analysis for a nonparametric test is more complex, and the sample size may need to be larger than for the parametric test.

The choice of the test should be made before the data are collected. The test should be specified in the study plan, and the power analysis should be based on that test. If the test is changed after the data are collected, the power analysis is no longer valid, and the sample size may be inadequate.

Step 3: Specify the Effect Size and the Standard Deviation

The effect size and the standard deviation are the two inputs that require the most judgment. The effect size should be the smallest effect that is biologically meaningful. The standard deviation should be the expected variability of the outcome within each group. Both should be estimated from the best available evidence.

The effect size can be estimated from the literature, from pilot data, or from a meta-analysis. The estimate should be specific to the outcome and the comparison. The standard deviation can be estimated from the same sources. If the standard deviation is not available, it can be estimated from the range of the data, or it can be based on the coefficient of variation from a similar study.

The estimates should be conservative. The effect size should be smaller than the effect that the researcher expects, and the standard deviation should be larger than the expected value. This approach ensures that the sample size is adequate even if the true effect is smaller or the variability is larger than expected.

Step 4: Calculate the Sample Size

The sample size can be calculated using a statistical software package, an online calculator, or a formula. The formula for a two-group t-test is based on the effect size, the standard deviation, the alpha level, and the desired power. The formula for an ANOVA is more complex and depends on the number of groups and the pattern of the differences among the groups.

The sample size should be calculated for the primary outcome and the primary comparison. The sample size should be the number of experimental units per group. The sample size should be rounded up to the nearest integer, and it should be increased to account for the expected loss of animals during the study.

The sample size should be reported in the study plan, along with the inputs that were used for the calculation. The report should include the effect size, the standard deviation, the alpha level, the desired power, and the resulting sample size. The report should also include the software and the version that was used for the calculation.

Step 5: Assess the Feasibility and Adjust the Design

The calculated sample size may not be feasible because of the cost, the availability of animals, or the ethical constraints. If the sample size is not feasible, the researcher can adjust the design to increase the power or to reduce the required sample size.

The design can be adjusted by reducing the variability of the outcome, by using a more efficient design, or by changing the primary outcome. The variability can be reduced by standardizing the conditions, by using a more precise measurement, or by using a paired design. The efficiency can be increased by using a balanced design, by using a factorial design, or by using a repeated-measures design.

The effect size can be changed by redefining the primary outcome or by changing the comparison. The effect size can be increased by using a more sensitive outcome or by using a more specific comparison. The effect size can also be increased by using a one-sided test, but this is only appropriate when the direction of the effect is known.

If the sample size is still not feasible, the researcher should consider whether the study can be conducted at all. The study should not be conducted with a sample size that is too small to detect the effect of interest. The researcher should instead consider a different design, a different outcome, or a different research question.

Choosing the Effect Size for Animal Studies

Using Pilot Data

Pilot data are the most reliable source for the effect size and the standard deviation. A pilot study is a small-scale version of the main study that is conducted to estimate the variability of the outcome and the feasibility of the protocol. The pilot study should use the same animals, the same measurements, and the same conditions as the main study. The pilot data should be used to estimate the standard deviation and the effect size.

The pilot study should be large enough to provide a stable estimate of the standard deviation. A pilot study with fewer than 10 animals per group may provide an unstable estimate. The pilot study should also be used to test the protocol, the measurement, and the data collection procedures. The pilot data should be reported in the study plan, and the estimates should be used for the power analysis.

The pilot data should be used with caution. The effect size estimated from the pilot data is likely to be larger than the true effect, because the pilot study is small and the estimate is subject to sampling error. The standard deviation estimated from the pilot data is also subject to sampling error. The power analysis should use a conservative estimate of the effect size and a conservative estimate of the standard deviation.

Using the Literature

The literature is a useful source for the effect size and the standard deviation when pilot data are not available. The effect size can be estimated from the results of previous studies that used the same outcome and the same comparison. The standard deviation can be estimated from the reported standard deviations or from the reported standard errors.

The literature should be used with caution. The effect size from a previous study may be larger than the true effect, because the literature is biased toward positive results. The standard deviation from a previous study may be smaller than the true standard deviation, because the previous study may have used more controlled conditions. The power analysis should use a conservative estimate of the effect size and a conservative estimate of the standard deviation.

The literature should be searched systematically, and the estimates should be based on the most relevant studies. The estimates should be documented in the study plan, and the rationale for the estimates should be stated. The estimates should be updated if new evidence becomes available.

The Minimum Biologically Meaningful Effect

The effect size should be the smallest effect that is biologically meaningful for the research question. This is not the effect that the researcher expects or the effect that the researcher hopes to find. It is the effect that would change the interpretation of the study or the decision that the study is designed to inform.

The minimum biologically meaningful effect should be defined in advance, before the data are collected. The definition should be based on the scientific context, the clinical relevance, or the regulatory requirement. The definition should be documented in the study plan, and the rationale for the definition should be stated.

The minimum biologically meaningful effect should be distinguished from the effect that is statistically significant. A statistically significant effect is an effect that is unlikely to be due to chance. A biologically meaningful effect is an effect that is large enough to matter. The power analysis should be based on the biologically meaningful effect, not the statistically significant effect.

Common Designs and Their Power Analysis

Two-Group Comparison

The two-group comparison is the most common design in animal studies. The design compares the outcome between two groups, such as a treatment group and a control group. The test is the t-test, and the power analysis is based on the difference between the means, the standard deviation, the alpha level, and the desired power.

The sample size per group is calculated using the formula for the t-test. The formula is based on the standardized effect size, which is the difference between the means divided by the standard deviation. The sample size increases as the effect size decreases, and the sample size increases as the desired power increases.

The two-group comparison can be balanced or unbalanced. A balanced design has the same number of animals in each group. An unbalanced design has different numbers of animals in each group. The balanced design is more efficient than the unbalanced design, and the power analysis should be based on the balanced design unless there is a reason for the unbalanced design.

One-Way ANOVA

The one-way ANOVA is used when the study has more than two groups. The test is used to compare the means of the groups. The power analysis is based on the effect size, the number of groups, the alpha level, and the desired power.

The effect size for the ANOVA is the effect size, which is the standard deviation of the group means divided by the standard deviation of the outcome. The effect size is a measure of the overall difference among the groups. The power analysis is based on the effect size, the number of groups, and the sample size per group.

The power analysis for the ANOVA is more complex than the power analysis for the t-test. The power analysis depends on the pattern of the differences among the groups. The power analysis can be based on the overall difference among the groups, or it can be based on the specific comparisons that will be made after the ANOVA.

The ANOVA is often followed by post-hoc tests to identify the groups that differ. The post-hoc tests require more power than the overall ANOVA, because the post-hoc tests are more specific. The power analysis should be based on the post-hoc tests if the post-hoc tests are the primary comparison.

Repeated-Measures Design

The repeated-measures design is used when the outcome is measured on the same animal at multiple time points. The design is more efficient than the independent-groups design, because the repeated measurements are correlated. The correlation between the measurements increases the power of the test.

The power analysis for the repeated-measures design is based on the effect size, the correlation between the measurements, the number of measurements, and the sample size. The power analysis is more complex than the power analysis for the independent-groups design, because the correlation must be specified.

The correlation between the measurements is the key input for the power analysis. The correlation can be estimated from the literature or from the pilot data. The correlation is typically positive, and the power increases as the correlation increases. The power analysis should use a conservative estimate of the correlation.

The repeated-measures design can be analyzed with a mixed-effects model. The mixed-effects model accounts for the correlation between the measurements and the variability between the animals. The power analysis for the mixed-effects model is based on the same inputs as the power analysis for the repeated-measures ANOVA.

Practical Implementation and Assessment Steps

Step 1: Document the Power Analysis in the Study Plan

The power analysis should be documented in the study plan before the study begins. The documentation should include the research question, the primary outcome, the statistical test, the effect size, the standard deviation, the alpha level, the desired power, and the calculated sample size. The documentation should also include the source of the effect size and the standard deviation, and the rationale for the choices.

The study plan should be reviewed by the institutional animal care and use committee (IACUC) as part of the protocol review. The IACUC reviews the study plan to ensure that the number of animals is justified and that the study is designed to answer the research question. The power analysis is a key part of the justification for the number of animals.

The study plan should be updated if the design is changed. The power analysis should be recalculated if the effect size, the standard deviation, the alpha level, or the desired power is changed. The updated power analysis should be documented in the study plan.

Step 2: Use the Power Analysis to Justify the Sample Size

The power analysis should be used to justify the sample size in the study plan and in the grant application. The justification should state the effect size, the standard deviation, the alpha level, the desired power, and the calculated sample size. The justification should also state the source of the effect size and the standard deviation.

The justification should be specific to the study. The justification should not be a general statement about the need for adequate power. The justification should state the specific inputs and the specific output of the power analysis.

The justification should be reviewed by the IACUC and by the funding agency. The IACUC should ensure that the sample size is the minimum number of animals that is needed to answer the research question. The funding agency should ensure that the sample size is adequate to detect the effect of interest.

Step 3: Conduct the Study and Record the Data

The study should be conducted according to the study plan. The data should be recorded for every experimental unit. The data should be recorded in a way that allows the analysis to be reproduced. The data should be recorded in a spreadsheet or a database, and the data should be checked for errors.

The data should be recorded for the primary outcome and for the other outcomes. The data should be recorded for the experimental unit, not for the individual measurements. The data should be recorded for the group, the treatment, and the time of the measurement.

The data should be recorded in a way that allows for the analysis to be reproduced. The data should be recorded in a format that can be imported into the statistical software. The data should be recorded with the variable names and the variable labels.

Step 4: Analyze the Data and Report the Results

The data should be analyzed using the statistical test that was specified in the study plan. The analysis should be conducted using the statistical software. The analysis should be conducted by the investigator or by a statistician.

The results should be reported in the research report. The report should include the sample size, the effect size, the standard deviation, the alpha level, the desired power, and the calculated sample size. The report should also include the observed effect size and the observed standard deviation.

The report should include the results of the statistical test. The report should include the test statistic, the degrees of freedom, the p-value, and the confidence interval. The report should also include the effect size and the confidence interval for the effect size.

Step 5: Evaluate the Power of the Study

The power of the study should be evaluated after the data are collected. The power should be calculated using the observed effect size and the observed standard deviation. The power should be compared to the desired power.

The power should be evaluated to determine whether the study was adequately powered. If the power is lower than the desired power, the study may have failed to detect a true effect. The results should be interpreted with caution, and the limitations of the study should be reported.

The power should be evaluated to determine whether the study was over-powered. If the power is much higher than the desired power, the study may have used more animals than necessary. The results should be interpreted with caution, and the sample size should be considered for future studies.

Records and Measurements

The Power Analysis Record

The power analysis record should include the following information: the research question, the primary outcome, the statistical test, the effect size, the standard deviation, the alpha level, the desired power, and the calculated sample size. The record should also include the source of the effect size and the standard deviation, and the rationale for the choices.

The record should be stored with the study plan and the protocol. The record should be available for review by the IACUC, the funding agency, and the journal. The record should be updated if the design is changed.

The record should be used for the reporting of the study. The record should be used to report the power analysis in the research report. The record should be used to justify the sample size in the protocol and in the grant application.

The Data Record

The data record should include the data for every experimental unit. The data record should include the group, the treatment, the outcome, and the time of the measurement. The data record should be checked for errors and for missing data.

The data record should be stored in a secure location. The data record should be stored in a format that can be used for the analysis. The data record should be stored for the duration of the study and for the period required by the institution or the funding agency.

The data record should be used for the analysis and for the reporting. The data record should be used to calculate the observed effect size and the observed standard deviation. The data record should be used to calculate the observed power.

The Analysis Record

The analysis record should include the statistical software, the version of the software, and the code that was used for the analysis. The analysis record should include the output of the analysis, including the test statistic, the degrees of freedom, the p-value, and the confidence interval.

The analysis record should be stored with the data record. The analysis record should be available for the review by the statistician and by the journal. The analysis record should be used for the reporting of the study.

The analysis record should be used for the reproducibility of the study. The analysis record should be used to reproduce the analysis and to verify the results. The analysis record should be used to the results of the study.

Common Failure Patterns in Power Analysis

Using the Observed Effect Size After the Study

A common failure is to use the observed effect size to calculate the power after the study is complete. This is called post-hoc power analysis. The observed effect size is the effect that was found in the study, and the power is the probability of finding that effect with the sample size. The post-hoc power is not useful, because the p-value already provides the information about the statistical significance.

The post-hoc power is often used to explain a non-significant result. The researcher may calculate the power and conclude that the study was underpowered. This conclusion is not valid, because the power is a function of the observed effect size, and the observed effect size is not the true effect size. The post-hoc power does not provide information about the probability of a true effect.

The power analysis should be conducted before the study, not after the study. The power analysis should be based on the effect size that is biologically meaningful, not the effect size that is observed. The power analysis should be used to determine the sample size, not to interpret the results.

Ignoring the Experimental Unit

A common failure is to ignore the experimental unit and to use the number of animals as the sample size when the unit is the cage or the pen. This inflates the sample size and the power. The analysis is then based on the number of animals, not the number of units, and the results are overconfident.

The experimental unit should be identified in the study plan. The unit is the unit to which the treatment is applied independently. The unit is the cage, the pen, or the animal. The sample size should be the number of units, not the number of animals.

The analysis should be conducted at the level of the unit. The analysis should account for the correlation between the animals in the same unit. The analysis should be conducted using a mixed-effects model or a generalized estimating equation.

Using an Unrealistic Effect Size

A common failure is to use an effect size that is too large. The effect size is the difference that the study is designed to detect. If the effect size is too large, the sample size will be too small, and the study will be underpowered. The study will fail to detect a smaller effect that is still biologically meaningful.

The effect size should be the smallest effect that is biologically meaningful. The effect size should be based on the literature, the pilot data, or the scientific judgment. The effect size should be conservative, and the sample size should be adequate to detect the effect.

The effect size should be justified in the study plan. The justification should state the source of the effect size and the rationale for the choice. The justification should be reviewed by the IACUC and by the funding agency.

Ignoring the Variability

A common failure is to ignore the variability of the outcome. The variability is the standard deviation of the outcome. If the variability is underestimated, the sample size will be too small, and the study will be underpowered. The study will fail to detect a true effect.

The variability should be estimated from the literature, the pilot data, or the scientific judgment. The variability should be conservative, and the sample size should be based on the conservative estimate. The variability should be justified in the study plan.

The variability can be reduced by standardizing the conditions, by using a more precise measurement, or by using a paired design. The variability should be considered in the design of the study, and the design should be chosen to minimize the variability.

Limitations and Interpretation

The Power Analysis Is an Estimate

The power analysis is an estimate, not a guarantee. The power analysis is based on the effect size, the standard deviation, the alpha level, and the desired power. These inputs are estimates, and the power analysis is only as good as the estimates. The power analysis does not guarantee that the study will detect the effect.

The power analysis should be interpreted with caution. The power analysis should be used to determine the sample size, but the sample size should be adjusted for the uncertainty in the estimates. The sample size should be increased if the estimates are uncertain.

The power analysis should be reported with the inputs and the output. The report should state the effect size, the standard deviation, the alpha level, the desired power, and the calculated sample size. The report should also state the source of the estimates and the rationale for the choices.

The Power Analysis Is Not a Substitute for the Scientific Judgment

The power analysis is a tool, not a substitute for the scientific judgment. The power analysis is based on the inputs that are provided by the researcher. The researcher must decide the effect size, the variability, the alpha level, and the desired power. The researcher must also decide the design of the study and the statistical test.

The power analysis should be used to inform the scientific judgment, not to replace it. The power analysis should be used to determine the sample size, but the sample size should be adjusted for the scientific judgment. The sample size should be increased if the scientific judgment suggests that the effect is smaller or the variability is larger.

The power analysis should be used to the design of the study. The power analysis should be used to determine the sample size, the design, and the statistical test. The power analysis should be used to the interpretation of the results.

The Power Analysis Is Not a Guarantee of the Reproducibility

The power analysis is not a guarantee of the reproducibility. The power analysis is based on the effect size, the variability, the alpha level, and the desired power. The power analysis does not guarantee that the study will be reproducible. The reproducibility depends on the design, the conduct, and the analysis of the study.

The power analysis should be used to ensure that the study is adequately powered. The power analysis should be used to determine the sample size, but the sample size is not the only factor that determines the reproducibility. The reproducibility also depends on the quality of the data, the analysis, and the reporting.

The power analysis should be used to ensure that the study is adequately powered. The power analysis should be used to determine the sample size, but the sample size should be adjusted for the uncertainty in the estimates. The power analysis should be used to ensure that the study is adequately powered to detect the effect of interest.

Reporting the Power Analysis

The Power Analysis in the Study Report

The power analysis should be reported in the study report. The report should include the effect size, the standard deviation, the alpha level, the desired power, and the calculated sample size. The report should also include the source of the effect size and the standard deviation, and the rationale for the choices.

The report should be written in a way that is clear and reproducible. The report should state the inputs and the output of the power analysis. The report should state the software and the version of the software that was used for the calculation.

The report should be reviewed by the statistician and by the journal. The report should be used to the results of the study. The report should be used to the interpretation of the study.

The Power Analysis and the Reporting Guidelines

The power analysis should be reported in accordance with the reporting guidelines. The reporting guidelines are the standards for the reporting of the research. The reporting guidelines are available from the EQUATOR Network, which is a repository of the reporting guidelines for the health research.

The reporting guidelines for the animal studies include the ARRIVE guidelines. The ARRIVE guidelines are the reporting guidelines for the animal research. The ARRIVE guidelines include the recommendation to report the sample size and the power analysis.

The reporting guidelines should be used to the reporting of the study. The reporting guidelines should be used to ensure that the study is reported in a way that is transparent and reproducible. The reporting guidelines should be used to ensure that the study is reported in a way that is useful to the reader.

The Power Analysis and the Publication Ethics

The power analysis should be reported in accordance with the publication ethics. The publication ethics are the standards for the publication of the research. The publication ethics are provided by the Committee on Publication Ethics (COPE).

The publication ethics include the requirement to report the research accurately and transparently. The publication ethics include the requirement to report the sample size and the power analysis. The publication ethics include the requirement to report the limitations of the study.

The publication ethics should be used to the reporting of the study. The publication ethics should be used to ensure that the study is reported accurately and transparently. The publication ethics should be used to ensure that the study is reported in a way that is the clear and reproducible.

Frequently Asked Questions

What is the minimum sample size for an animal study?

The minimum sample size depends on the effect size, the standard deviation, the alpha level, and the desired power. There is no universal minimum sample size. The sample size should be calculated using a power analysis based on the specific research question and the specific outcome. A study with a small effect size or a large standard deviation will require a larger sample size than a study with a large effect size or a small standard deviation.

How do I choose the effect size for a power analysis?

The effect size should be the smallest effect that is biologically meaningful for the research question. The effect size can be estimated from the literature, the pilot data, or the scientific judgment. The effect size should be conservative, and the sample size should be based on the conservative estimate. The effect size should be justified in the study plan.

What is the difference between the sample size and the number of animals?

The sample size is the number of experimental units per group. The experimental unit is the unit to which the treatment is applied independently. The unit can be the animal, the cage, or the pen. The number of animals can be larger than the sample size if the unit is the cage or the pen. The power analysis should be based on the number of units, not the number of animals.

What is post-hoc power and why is it not useful?

Post-hoc power is the power that is calculated using the observed effect size after the study. The post-hoc power is not useful because the observed effect size is not the true effect size. The post-hoc power does not provide the information about the power of the study. The power analysis should be conducted before the study, not after the study.

How do I account for the loss of animals in the power analysis?

The sample size should be increased to account for the loss of animals. The loss can be due to the death, the illness, or the exclusion of the animals. The expected loss should be estimated from the literature or the pilot data. The sample size should be increased by the expected loss.

What is the role of the IACUC in the power analysis?

The IACUC reviews the study protocol to ensure that the use of the animals is appropriate. The IACUC reviews the power analysis to ensure that the sample size is the minimum number of animals needed to answer the research question. The IACUC should approve the sample size and the power analysis before the study begins.

What should I do if the calculated sample size is not feasible?

If the calculated sample size is not feasible, the researcher should adjust the design. The researcher can reduce the variability, use a more efficient design, or change the primary outcome. The researcher can also redefine the effect size or the comparison. If the sample size is still not feasible, the researcher should consider whether the study can be conducted.

How do I report the power analysis in a manuscript?

The power analysis should be reported in the methods section of the manuscript. The report should include the effect size, the standard deviation, the alpha level, the desired power, and the calculated sample size. The report should also include the source of the effect size and the standard deviation, and the rationale for the choices.

Using the Evidence

Source	Best use in this topic	Important limitation
Research Methods Resources	official guidance	Check the linked page for current local requirements
EQUATOR Network	official guidance	Check the linked page for current local requirements
Core Practices	official guidance	Check the linked page for current local requirements

Related Bioinformatics Guides

Related Clinical & Scientific Guides

References and Further Reading

Research Methods Resources. National Library of Medicine.
EQUATOR Network. EQUATOR Network.
Core Practices. Committee on Publication Ethics.
NIH Grants and Funding. National Institutes of Health.
ORCID for Researchers. ORCID.
Data Management and Sharing Policy. National Institutes of Health.
NCBI Data Resources. National Center for Biotechnology Information.
EMBL-EBI Training. European Bioinformatics Institute.
Simulation methodologies to determine statistical power in laboratory animal research studies.. Laboratory animals, 2024.
Statistical power analysis of cardiovascular safety pharmacology studies in conscious rats.. Journal of pharmacological and toxicological methods, 2016.
Statistical power analysis for hemodynamic cardiovascular safety pharmacology studies in beagle dogs.. Journal of pharmacological and toxicological methods, 2004.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.