Common mistakes in statistical analysis biology

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

Key Takeaways

• Pseudoreplication arises from treating subsamples (e.g., technical replicates of gene expression from a single mouse) as independent biological replicates, artificially inflating sample size and leading to false significance. Correct identification of the true experimental unit (e.g., the individual mouse) and use of nested or mixed-effects models are crucial preventative measures.
• Violated parametric test assumptions, particularly normality and homoscedasticity, invalidate statistical inference. For instance, applying a t-test to skewed gene expression data without checking assumptions can yield incorrect p-values and confidence intervals. Pre-analysis checks (e.g., Shapiro-Wilk, Levene tests) and consideration of nonparametric alternatives (e.g., Mann-Whitney U) are essential.
• Misinterpreting p-values as the probability of the null hypothesis being true, rather than the probability of observing data as extreme given the null, leads to overconfidence in findings. Reporting exact p-values alongside effect sizes and confidence intervals (e.g., reporting a mean difference of 5 units with a 95% CI of [<a href="#ref-1">1</a>, <a href="#ref-2">2</a>] and p=0.03) provides a more nuanced understanding of the evidence.
• Multiple comparisons without correction significantly inflates the false-positive rate; testing thousands of genes for differential expression without controlling the False Discovery Rate (FDR) will inevitably yield spurious associations. Methods like Benjamini-Hochberg are necessary to manage this risk.
• HARKing (Hypothesizing After Results are Known) and p-hacking distort the scientific record by presenting post-hoc discoveries as pre-planned hypotheses, increasing the likelihood of irreproducible findings. Pre-registering hypotheses and analysis plans before data collection is the most effective strategy to mitigate these biases.

Quick Answer

• Statistical errors in biology research typically stem from pseudoreplication, violated test assumptions, and misinterpreted p-values, each producing misleading conclusions that can undermine publication integrity.
• Adopt a pre-registered analysis plan, verify assumptions before selecting tests, and treat p-values as continuous evidence instead of binary significance thresholds.
• No statistical correction can rescue fundamentally flawed experimental design, so consult a biostatistician before data collection begins.

At a Glance

Why Statistical Errors Persist in Biology

Statistical analysis in biology carries a distinct burden. Biological systems exhibit high variability, small sample sizes are common due to cost or ethics, and measurements often come from nested or clustered designs. These conditions create fertile ground for errors that would be obvious in other disciplines. The consequences are not trivial. Flawed statistics contribute to irreproducible findings, wasted resources, and in some cases retractions that damage careers and public trust in science.

The problem is compounded by a training gap. Many biology students receive only a single introductory statistics course, often focused on t-tests and ANOVA, without exposure to modern resampling methods, mixed-effects modeling, or the philosophy of inference. When researchers encounter complex data, they default to familiar tools, even when those tools are inappropriate. The result is a literature populated with analyses that are technically executed but conceptually invalid.

This article identifies the most common statistical mistakes in biology research, explains why they occur, and provides practical solutions. The focus is on decisions that researchers make daily: choosing a test, interpreting output, and reporting results. Each section includes concrete management steps, records to keep, and criteria for escalating to professional help.

Pseudoreplication: The Hidden Sample Size Problem

Defining the True Experimental Unit

Pseudoreplication occurs when a researcher treats subsamples as independent replicates when they are not. The classic example is a single aquarium with ten fish, where each fish is counted as a sample. In reality, the aquarium is the experimental unit because all fish share the same water, temperature, and tank effects. The ten fish are subsamples, not independent replicates.

This mistake inflates the effective sample size, making the test appear more powerful than it is. The result is a p-value that is artificially small, leading to a conclusion that may not hold when the experiment is repeated with new tanks. The problem is pervasive in ecology, physiology, and molecular biology, where technical replicates are often confused with biological replicates.

How to Identify Pseudoreplication

Ask a simple question: if you repeated the experiment, what would you redo? If you would set up a new tank, a new field plot, or a new patient, then that is the experimental unit. If you would simply measure the same tank again, you are dealing with subsampling.

Consider a study measuring gene expression in three mice per treatment group. If each mouse is sampled once, the sample size is three. If the researcher runs the assay in triplicate for each mouse, the sample size is still three, not nine. The triplicates are technical replicates that reduce measurement error, but they do not increase the number of independent observations.

Practical Steps to Avoid Pseudoreplication

1. Define the experimental unit in writing before data collection.
2. Count the number of independent units, not the number of measurements.
3. Use nested or mixed-effects models when subsampling is unavoidable.
4. Report the number of experimental units and the number of subsamples separately.

Records and Measurements

Keep a design log that states the experimental unit, the number of units per treatment, and the number of subsamples per unit. This log should be written before data collection and stored with the raw data. If a reviewer questions the sample size, the log provides the justification.

Escalation Criteria

If you are unsure whether your design has pseudoreplication, consult a biostatistician before you collect data. Once the data is collected, the problem cannot be fully corrected. A statistician may be able to apply a mixed-effects model that accounts for clustering, but the power of the study will be lower than originally planned.

Ignoring Test Assumptions

Normality and Homoscedasticity

Parametric tests such as t-test and ANOVA rely on assumptions of normality and homogeneity of variance. When these assumptions are violated, the test statistics are not valid, and the p-values are unreliable. The problem is common in biology because many measurements are skewed, such as gene expression, enzyme activity, and cell counts.

Researchers often skip assumption checks because they are not taught to run them, or they assume that the Central Limit Theorem will save them. The Central Limit Theorem applies to the sampling distribution of the mean, not to the raw data. For small sample sizes, which are common in biology, the theorem does not guarantee normality.

How to Check Assumptions

1. Visual inspection: use Q-Q plots and histograms.
2. Formal tests: Shapiro-Wilk for normality, Levene for variance equality.
3. If assumptions are violated, use nonparametric alternatives like Mann-Whitney U or Kruskal-Wallis.
4. Consider transformations (log, square root) but report them clearly.

The Problem with Transformations

Transformations can fix normality but complicate interpretation. A log-transformed mean is not the same as the original mean. If you transform data, report the back-transformed values and explain the transformation in the methods. Do not transform data solely to get a significant p-value. That is a form of p-hacking.

Records and Failure

Record the results of assumption checks in your analysis log. If a reviewer asks why you used a nonparametric test, you can show the Shapiro-Wilk output. If you do not check assumptions, you cannot justify your choice of test.

Misinterpreting P-Values

What a P-Value Actually Means

A p-value is the probability of observing data as extreme as what you collected, assuming the null hypothesis is true. It is not the probability that the null hypothesis is true. It is not the probability that your alternative hypothesis is correct. It is not the probability that your result is due to chance.

The common misinterpretation is to treat p < 0.05 as proof that a finding is real. This is wrong. A p-value of 0.04 means that if the null hypothesis were true, you would see data this extreme 4% of the time. It does not mean there is a 96% chance the effect is real.

The Problem with the 0.05 Threshold

The 0.05 threshold is arbitrary. It was popularized by Ronald Fisher in the 1920s, but Fisher himself did not intend it to be a hard cutoff. He suggested that p-values should be reported and interpreted in context. The binary "significant" or "not significant" framing loses information and encourages p-hacking.

Practical Steps to Interpret P-Values

1. Report the exact p-value, beyond whether it is below 0.05.
2. Report effect sizes and confidence intervals.
3. Interpret the p-value in the context of the study design and prior evidence.
4. Do not use the phrase "trending toward significance" for p-values between 0.05 and 0.10.

Records and Failure

Keep a table of all p-values, effect sizes, and confidence intervals for each test. This table will help you see the pattern across your results. If you have many p-values near 0.05, you may have a multiple comparisons problem.

Multiple Comparisons and the False Discovery Rate

The Problem of Many Tests

When you run many statistical tests, the probability of at least one false positive increases. If you run 20 tests at alpha = 0.05, the chance of at least one false positive is about 64%. This is the multiple comparisons problem. It is common in genomics, where thousands of genes are tested simultaneously, but it also occurs in simpler studies with many outcome variables.

Correction Methods

1. Bonferroni: divide alpha by the number of tests. This is conservative and may miss real effects.
2. Holm: a stepwise method that is less conservative than Bonferroni.
3. Benjamini-Hochberg (FDR): controls the false discovery rate, which is the proportion of false positives among the rejected hypotheses. This is preferred in genomics.

Practical Steps

1. Decide on the correction method before you run the tests.
2. Report the number of tests performed.
3. Report the corrected p-values.
4. Do not cherry-pick the tests that are significant.

Records and Failure

Keep a list of all tests performed, including those that were not significant. This list is part of your analysis log. If you do not report all tests, you are hiding the multiple comparisons problem.

HARKing and P-Hacking

Hypothesizing After the Results Are Known

HARKing is the practice of presenting a hypothesis after the results are known, as if it had been predicted in advance. This is a form of scientific misconduct because it misrepresents the research process. It is common in exploratory studies, where researchers look at the data, find an interesting pattern, and then write the paper as if they had predicted it.

The problem is that HARKing inflates the false positive rate. If you test many hypotheses and only report the ones that are significant, the literature will be filled with findings that are not real.

P-Hacking: The Many Ways to Get a Significant P-Value

P-hacking is the practice of manipulating the analysis until a p-value is below 0.05. This can include:

• Removing outliers without justification
• Adding or removing covariates
• Trying different transformations
• Splitting or combining groups
• Stopping data collection when the p-value is significant

P-hacking is not always intentional. It can be a result of not having a pre-specified analysis plan. When a researcher tries several analyses and reports only the one that works, the p-value is no longer valid.

The Solution: Pre-Registration

Pre-registration is the practice of writing a detailed analysis plan before data collection. The plan includes the hypotheses, the sample size, the primary outcome, and the statistical tests. The plan is time-stamped and stored in a public repository. This prevents HARKing and p-hacking because the analysis is fixed in advance.

The National Institutes of Health (NIH) encourages rigorous study design and transparent reporting. Pre-registration is a practical way to demonstrate that your analysis was planned, not post-hoc.

Practical Steps

1. Write a pre-registration document before data collection.
2. Include the hypotheses, sample size, and analysis plan.
3. Submit the document to a public repository.
4. If you deviate from the plan, explain why in the paper.

Records and Failure

Keep a copy of the pre-registration document with your data. If you deviate from the plan, record the deviation and the reason. This record will be useful when you write the paper and when reviewers ask questions.

Data Leakage and Improper Data Splitting

The Problem of Information Leakage

Data leakage occurs when information from the test set is used to train the model. This is a common problem in machine learning, but it also occurs in traditional statistics. For example, if you use the entire dataset to select features and then test the model on the same data, the results will be overly optimistic.

In biology, data leakage can occur when you normalize the data using the entire dataset, including the test samples. This is a problem in genomics, where normalization is a common step. If the normalization is done before splitting the data, the test samples have influenced the normalization, and the model is not truly independent.

Practical Steps to Avoid Data Leakage

1. Split the data into training and test sets before any preprocessing.
2. Apply normalization and feature selection only to the training set.
3. Use cross-validation to assess model performance.
4. Report the model performance on the test set only.

Records and Failure

Record the exact steps of your data processing pipeline. If you use a machine learning model, record the random seed used for the split. This will allow you to reproduce the analysis.

Inadequate Reporting of Statistical Methods

The Problem of Vague Methods

Many papers report statistics with a single sentence: "Data were analyzed using a t-test." This is insufficient. The reader cannot tell which t-test was used, whether it was one-tailed or two-tailed, whether the assumptions were checked, or what software was used. This makes the analysis impossible to reproduce.

The Solution: Reporting Guidelines

The EQUATOR Network provides reporting guidelines for many study types. These guidelines specify what should be reported in the methods and results sections. For example, the ARRIVE guidelines for animal research require a detailed description of the statistical methods, including the sample size calculation, the statistical tests used, and the software.

Practical Steps

1. Identify the appropriate reporting guideline for your study type.
2. Follow the guideline when writing the methods and results.
3. Report the software and version used for the analysis.
4. Report the exact p-values, effect sizes, and confidence intervals.

Records and Failure

Keep a copy of the reporting guideline you followed. This will be useful when you write the paper and when you respond to reviewers.

The Role of Data Management and Sharing

The Importance of Data Sharing

Data sharing is essential for reproducibility. If other researchers cannot access your data, they cannot verify your results. The NIH Data Management and Sharing Policy requires researchers to plan for data sharing and to submit a data management plan with their grant application.

Practical Steps

1. Write a data management plan before you start the study.
2. Store the data in a public repository.
3. Provide a data dictionary that explains the variables.
4. Share the analysis code.

Records and Failure

Keep a copy of the data management plan and the data dictionary. These documents will be required when you publish the paper.

The Importance of Researcher Identity and ORCID

Why ORCID Matters

ORCID provides a persistent digital identifier for researchers. This is important for statistical analysis because it ensures that the researcher is correctly identified in the literature. When a researcher changes their name or institution, the ORCID identifier remains the same. This is useful for tracking the researcher's publications and for ensuring that the researcher is credited for their work.

Practical Steps

1. Create an ORCID identifier.
2. Link the ORCID to your publications.
3. Use the ORCID in your grant applications and manuscript submissions.

Records and Failure

Keep a record of your ORCID identifier. This will be used in your publications and grant applications.

Common Failure Patterns in Statistical Analysis

The "Significant" Obsession

Many researchers are obsessed with getting a p-value below 0.05. This obsession leads to p-hacking, HARKing, and the misinterpretation of results. The solution is to focus on the effect size and the confidence interval, not the p-value.

The "One-Size-Fits-All" Approach

Some researchers use the same statistical test for all their data, regardless of the design. This is a failure because different designs require different tests. For example, a paired design requires a paired t-test, not an independent t-test.

The "Black Box" Approach

Some researchers use a statistical software package without understanding what the software is doing. This is a failure because the software may be using a method that is not appropriate for the data. The researcher should understand the assumptions of the test and the output of the software.

The "Data Dredging" Approach

Some researchers collect data without a clear hypothesis and then look for patterns. This is a failure because the patterns may be due to chance. The researcher should have a hypothesis before the data is collected.

Practical Workflow for a Statistical Analysis

Step 1: Define the Research Question

The research question should be specific and testable. It should include the population, the outcome, and the comparison.

Step 2: Design the Study

The study design should be appropriate for the research question. The design should include the sample size, the experimental unit, and the control group.

Step 3: Write the Pre-Registration Document

The pre-registration document should include the hypotheses, the sample size, the primary outcome, and the statistical analysis.

Step 4: Collect the Data

The data should be collected according to the study design. The data should be recorded in a clear and organized manner.

Step 5: Check the Assumptions

The assumptions of the statistical test should be checked before the test is performed. If the assumptions are violated, a different test should be used.

Step 6: Perform the Analysis

The analysis should be performed according to the pre-registration document. The analysis should be recorded in a clear and organized manner.

Step 7: Report the Results

The results should be reported according to the reporting guidelines. The results should include the effect size, the confidence interval, and the p-value.

Step 8: Share the Data

The data should be shared in a public repository. The data should be accompanied by a data dictionary.

The Role of the Biostatistician

When to Consult a Biostatistician

A biostatistician should be consulted before the study is designed. The biostatistician can help with the sample size, the study design, and the analysis plan. The biostatistician can also help with the interpretation of the results.

The Cost of Not Consulting

The cost of not consulting a biostatistician is high. A study with a flawed design cannot be fixed after the data is collected. The study may be wasted, and the researcher may have to start over.

The Escalation Criteria

If you are unsure about any aspect of the statistical analysis, you should consult a biostatistician. This includes the choice of the test, the interpretation of the results, and the reporting of the methods.

The Future of Statistical Analysis in Biology

The Move Toward Open Science

The move toward open science is changing the way statistical analysis is done. Researchers are sharing their data, their code, and their pre-registration documents. This is making the analysis more transparent and more reproducible.

The Use of Machine Learning

Machine learning is becoming more common in biology. This is a powerful tool, but it is also a source of new statistical errors. The researcher must be careful to avoid data leakage and to validate the model properly.

The Importance of Statistical Literacy

Statistical literacy is essential for all biology researchers. The researcher must understand the basics of statistics, including the assumptions of the tests, the interpretation of the p-value, and the importance of the effect size.

Conclusion

Statistical analysis is a critical part of biology research. The mistakes described in this article are common, but they are avoidable. The researcher must be careful to define the experimental unit, check the assumptions, interpret the p-value correctly, and report the results transparently. The researcher must also be willing to consult a biostatistician when necessary. By following these steps, the researcher can produce results that are reliable and reproducible.

A Decision Framework for Choosing the Correct Statistical Test

The Core Problem: Test Selection Without a Structured Process

Researchers in biology frequently select statistical tests based on habit, convenience, or what they used in a previous paper. This approach leads to mismatched tests, invalid inferences, and results that cannot be reproduced. The decision of which test to use is not a matter of preference. It is a logical process that depends on the structure of the data, the research question, and the assumptions that can be met. Without a structured framework, researchers default to familiar tests such as the t-test or ANOVA, even when the data are paired, clustered, or non-normal.

The consequences of a poor test choice are severe. A test that assumes independence when data are clustered produces p-values that are too small. A test that assumes normality when data are skewed produces invalid confidence intervals. A test that ignores the direction of the hypothesis can obscure a real effect. These errors are not caught by peer review because reviewers often lack access to the raw data and the analysis log. The only way to prevent these errors is to make the test selection process explicit, documented, and reproducible.

The Five-Question Decision Framework

A practical decision framework can be applied to any dataset before a test is chosen. The framework consists of five questions that the researcher must answer in writing. The answers determine the family of tests that are appropriate. The framework is not a substitute for a biostatistician, but it is a tool that helps the researcher identify when professional help is needed.

Question 1: What Is the Research Question Type?

The research question determines the structure of the analysis. There are three common types in biology:

1. Difference questions: Is there a difference between groups? Examples include comparing treatment and control groups, comparing time points, or comparing genotypes.
2. Association questions: Is there a relationship between two variables? Examples include the relationship between body mass and metabolic rate, or between gene expression and environmental temperature.
3. Prediction questions: Can we predict an outcome from one or more predictors? Examples include predicting survival from clinical variables or predicting species distribution from environmental variables.

The answer to this question narrows the test family. Difference questions lead to t-tests, ANOVA, or their nonparametric equivalents. Association questions lead to correlation or regression. Prediction questions lead to regression, logistic regression, or machine learning methods.

Question 2: What Is the Scale of the Outcome Variable?

The outcome variable is the variable that is measured or observed. Its scale determines the type of test that can be used.

• Continuous: The variable can take any value within a range. Examples include body mass, gene expression, and enzyme activity. Continuous variables can be analyzed with parametric tests if assumptions are met.
• Ordinal: The variable has ordered categories. Examples include disease severity scores, behavioral ratings, and developmental stages. Ordinal variables require nonparametric tests or ordinal regression.
• Nominal: The variable has categories with no order. Examples include species, sex, and treatment group. Nominal variables require chi-square tests, Fisher exact tests, or logistic regression.

The scale of the outcome variable is often ignored. Researchers treat ordinal scores as continuous and apply t-tests. This is a mistake because the distance between categories is not known. A score of 2 is not necessarily twice a score of 1. The test will be invalid because the assumptions of the test are not met.

Question 3: How Many Groups Are Being Compared?

The number of groups determines whether a two-sample test or a multi-group test is needed.

• Two groups: Use a t-test (independent or paired) or a Mann-Whitney U test (independent) or a Wilcoxon signed-rank test (paired).
• Three or more groups: Use a one-way ANOVA or a Kruskal-Wallis test. If the groups are related, use a repeated-measures ANOVA or a Friedman test.

The number of groups is often confused with the number of variables. A study with two variables and two groups still uses a two-group test. A study with one variable and three groups uses a one-way ANOVA.

Question 4: Are the Observations Independent?

Independence is the most critical assumption in statistics. Observations are independent if the value of one observation does not influence the value of another. In biology, independence is often violated because of clustering, repeated measures, or spatial autocorrelation.

• Independent: Each observation comes from a different experimental unit. Use standard tests.
• Paired or repeated: Each observation is matched with another. Use paired t-tests, repeated-measures ANOVA, or mixed-effects models.
• Clustered: Observations are grouped within a larger unit. Use mixed-effects models or generalized estimating equations.

The answer to this question is often the source of pseudoreplication. If the researcher does not recognize the clustering, they will use a test that assumes independence and the p-values will be too small.

Question 5: What Are the Distributional Assumptions?

The final question is about the distribution of the data. The researcher must check whether the data meets the assumptions of the test. The assumptions are:

• Normality: The data is normally distributed. Check with a Shapiro-Wilk test or a Q-Q plot.
• Homogeneity of variance: The variance is equal across groups. Check with a Levene test.
• Independence: The observations are independent. This is checked by the study design.

If the assumptions are met, use a parametric test. If the assumptions are violated, use a nonparametric test or a transformation.

Applying the Framework: A Worked Example

Consider a researcher who wants to compare the body mass of mice in three different diet groups. The researcher has 10 mice per group, and each mouse is measured once.

• Question 1: The research question is a difference question. The researcher wants to know if the diets differ.
• Question 2: The outcome variable is body mass, which is continuous.
• Question 3: There are three groups.
• Question 4: The observations are independent because each mouse is a separate experimental unit.
• Question 5: The researcher checks normality and homogeneity of variance. If the assumptions are met, the researcher uses a one-way ANOVA. If the assumptions are violated, the researcher uses a Kruskal-Wallis test.

The framework leads to a clear choice. Without the framework, the researcher might have used a t-test, which would be wrong because there are three groups.

The Decision Tree as a Record

The five-question framework should be documented in the analysis log. The researcher should write down the answers to each question and the resulting test choice. This record serves several purposes:

• It provides a justification for the test choice when reviewers ask.
• It allows the researcher to reproduce the analysis.
• It identifies the point where a biostatistician should be consulted.

The record should be stored with the raw data and the analysis code. It should be written before the analysis is run, not after. If the researcher changes the test after seeing the results, the change must be documented and justified.

The Framework in Practice: A Troubleshooting Guide

The framework can also be used to troubleshoot an existing analysis. If a reviewer questions the choice of test, the researcher can go through the five questions and show that the test is appropriate. If the researcher cannot answer the questions, the analysis is likely flawed.

The following troubleshooting steps are useful:

1. Write down the research question in one sentence.
2. Write down the outcome variable and its scale.
3. Write down the number of groups and the number of observations per group.
4. Write down the experimental unit and whether the observations are independent.
5. Check the assumptions of the test.

If any of these steps is unclear, the analysis is at risk. The researcher should consult a biostatistician before proceeding.

The Framework and the Reporting Guidelines

The framework aligns with the reporting guidelines provided by the EQUATOR Network. The guidelines require that the statistical methods be described in enough detail to be reproduced. The five-question framework provides the structure for that description. The researcher can report the answers to the questions in the methods section, which will satisfy the reporting requirements.

For example, the ARRIVE guidelines for animal research require a description of the statistical methods, including the sample size, the statistical tests, and the software. The five-question framework provides the information needed to write this description.

The Framework and the Data Management Plan

The framework also aligns with the NIH Data Management and Sharing Policy. The policy requires a data management plan that describes how the data will be collected, stored, and shared. The framework can be included in the plan as a description of the analysis. This ensures that the analysis is transparent and reproducible.

The Framework and the Publication Ethics

The framework supports the publication ethics described by the Committee on Publication Ethics. The framework makes the analysis transparent, which reduces the risk of p-hacking and HARKing. The researcher who uses the framework can demonstrate that the test was chosen before the results were known.

The Framework and the Researcher Identity

The framework is a tool that the researcher can use throughout their career. It is not tied to a specific software package or a specific statistical method. It is a way of thinking about data that can be applied to any research question. The researcher can use the framework to teach students, to review the work of others, and to plan new studies.

The Framework and the Biostatistician

The framework is not a substitute for a biostatistician. It is a tool that helps the researcher identify when a biostatistician is needed. If the researcher cannot answer any of the five questions, the researcher should consult a biostatistician. The biostatistician can help with the design, the analysis, and the interpretation.

The framework is also useful for the biostatistician. The biostatistician can use the framework to understand the research question and the data. This makes the consultation more efficient and more effective.

The Framework and the Future of Statistics

The framework is a simple tool, but it is a powerful one. It is a way of thinking about data that is not tied to a specific software or a specific method. It is a way of thinking that is the foundation of statistical literacy. The framework is a tool that the researcher can use for the rest of their career.

The framework is not a replacement for the statistical knowledge. The researcher must still understand the assumptions of the tests and the interpretation of the results. The framework is a way to organize that knowledge and to apply it in a consistent way.

The Framework and the Common Failure Patterns

The framework addresses the common failure patterns that are described in the article. The "one-size-fits-all" approach is addressed by the framework because the framework forces the researcher to consider the design and the data. The "black box" approach is addressed by the framework because the framework requires the researcher to understand the test. The "data dredging" approach is addressed by the framework because the framework requires the researcher to define the research question before the analysis.

The Framework and the Practical Workflow

The framework is a part of the practical workflow. The workflow is a series of steps that the researcher follows from the research question to the data sharing. The framework is the step that is the analysis. The framework is the step that is the test selection.

The workflow is:

1. Define the research question.
2. Design the study.
3. Write the pre-registration document.
4. Collect the data.
5. Check the assumptions.
6. Perform the analysis.
7. Report the results.
8. Share the data.

The framework is the step 6. The framework is the step that is the analysis. The framework is the step that is the test selection.

The Framework and the Future

The framework is a tool that is the future. The future of statistics is the open science. The open science is the sharing of the data and the code. The framework is the sharing of the analysis. The framework is the sharing of the decision.

The framework is a tool that is the future of the statistics. The framework is a tool that is the future of the biology. The framework is a tool that is the future of the research.

The Framework and the Conclusion

The framework is a practical tool that the researcher can use to select the correct statistical test. The framework is a five-question process that is the research question, the outcome variable, the number of groups, the independence, and the assumptions. The framework is a record that is the analysis. The framework is a troubleshooting tool that is the analysis. The framework is a tool that is the researcher.

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Frequently Asked Questions

What is the most common statistical mistake in biology research?

The most common mistake is pseudoreplication, where subsamples are treated as independent replicates. This inflates the sample size and leads to false significance. The solution is to define the experimental unit before data collection and use nested models when subsampling is unavoidable.

How can I check if my data is normally distributed?

You can use a Shapiro-Wilk test or a Q-Q plot. The Shapiro-Wilk test is a formal test of normality, while the Q-Q plot is a visual inspection. If the data is not normal, you can use a nonparametric test or transform the data.

What is the difference between a p-value and an effect size?

A p-value is the probability of observing the data under the null hypothesis. An effect size is the magnitude of the difference or relationship. The p-value does not tell you the size of the effect. You should report both the p-value and the effect size.

What is the false discovery rate?

The false discovery rate is the proportion of false positives among the discoveries. It is controlled by the Benjamini-Hochberg method. This method is preferred in genomics data, where many tests are performed.

What is pre-registration and why is it important?

Pre-registration is the practice of writing a study plan before the data is collected. The plan includes the hypotheses, the sample size, and the analysis. It is important because it prevents HARKing and p-hacking.

What is data leakage?

Data leakage is the use of information from the test set in the training set. It leads to optimistic results. The solution is to split the data before any preprocessing.

What is the role of the biostatistician?

The biostatistician helps with the study design, the sample size, and the analysis plan. The biostatistician should be consulted before the data is collected.

What is the NIH Data Management and Sharing Policy?

The NIH Data Management and Sharing Policy requires researchers to share data and submit a data management plan. The policy is designed to make research more reproducible.

Using the Evidence

Related Bioinformatics Guides

• Genomic Data Analysis Tools: A Comparative Guide for Researchers
• Metabolomics Data Analysis Workflow: From Raw Data to Biological Insight
• Metagenomics Data Analysis: From Raw Reads to Biological Insights
• Proteomics Data Analysis Workflow: From Raw Spectra to Biological Insights
• Spatial Omics Data Analysis: From Image Processing to Biological Interpretation

Related Clinical & Scientific Guides

• A Practical Guide to Detecting Antimicrobial Resistance Genes in Shotgun Metagenomic Data
• Computational Immunology: Modeling the Immune System
• How to Set Hard Filters for Germline Variant Calling: A Practical Guide to GATK Best Practices

References and Further Reading

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