Statistics give meaning to data collected during research and make it simple to extract actionable insights from the data. As a result, it’s important to have a guide for analyzing data, which is where a statistical analysis plan (SAP) comes in.

A statistical analysis plan provides a framework for collecting data, simplifying and interpreting it, and assessing its reliability and validity.

Here’s a guide on what a statistical analysis plan is and how to write one.

What Is a Statistical Analysis Plan?

A statistical analysis plan (SAP) is a document that specifies the statistical analysis that will be performed on a given dataset. It serves as a comprehensive guide for the analysis, presenting a clear and organized approach to data analysis that ensures the reliability and validity of the results.

SAPs are most widely used in research, data science, and statistics. They are a necessary tool for clearly communicating the goals and methods of analysis, as well as documenting the decisions made during the analysis process.

SAPs typically outline the steps needed to prepare data for analysis, the methods to use, and how details such as sample size, data sources, and any assumptions or limitations of the analysis.

The first step in creating a statistical analysis plan is to identify the research question or hypothesis you’re testing. 

Next, choose the appropriate statistical techniques for analyzing the data and specify the analysis details, such as sample size and data sources. It should also include the strategy for presenting and interpreting the results.

How to Develop a Statistical Analysis Plan

Here are the steps for creating a successful statistical analysis plan (SAP):

• Identify the Research Question or Hypothesis

This is the main goal of the analysis, and it will guide the rest of the SAP. Here are the steps to identifying research questions or hypotheses:

• Define the Analysis’s Goal

The research question or hypothesis should be related to the analysis’s main goal or purpose. If the goal is to evaluate the effectiveness of a content strategy, the research question could be “Is the new strategy more effective than the previous or standard strategy?”

• Determine the Variables of Interest

Determine which variables are important to the research question or hypothesis. In the preceding example, the variables could include the effectiveness of the content strategy and its drawbacks.

• Formulate the Question or Hypothesis

After identifying the variables, use them to research the question in a clear and precise way. For example, “is the new content strategy more effective than the current one in terms of user acquisition?

• Check for Clarity and Specificity

Review the research question or hypothesis for precision and clarity. If a question isn’t well-structured enough to be tested with the data and resources at hand, revise it.

• Determine the Sample Size

The main factors that influence the sample size are the type of data being analyzed and the resources available. For example, if the data is continuous, you’ll probably need a large sample size.

Also, your sample size should be tailored to your available resources, time, and budget. You could also calculate the sample size using a sample size formula or software.

• Select the Appropriate Statistical Techniques

Choose the most appropriate statistical techniques for the analysis based on the research question, data type, and sample size.

• Specify the Details of the Analysis

This includes the data sources, any analysis assumptions or limitations, and any variables that need modifications.

• Plan For Presenting and Interpreting the Results

Plan how the results will be interpreted and communicated to your audience. Choose how you want to present the information, such as a report or a presentation.

Identifying the Need for a Statistical Analysis Plan

Here are some real-world examples of where a statistical analysis plan is needed:

• Research Studies

Health researchers need SAP to determine the effectiveness of a new drug in treating a specific medical condition. It also outlines the methods and procedures for analyzing the study’s data, including sample size, data sources, and statistical techniques to be used.

• Clinic Trials

Clinical trials help to test the safety and efficacy of new medical treatments, which would necessitate gathering a large amount of data on how patients respond to treatment, side effects, and comparisons to existing treatments. 

A clinic trial SAP should emphasize the statistical analysis that will be performed on the trial data, such as sample size, data sources, and statistical techniques to be used.

• Data-Driven Projects

SAP is used by marketing research firms to outline the statistical analysis that will be performed on market research data. It specifies the sample size, data sources, and statistical techniques that will be used to analyze data and provide insights into consumer behavior.

• Government Agencies

When government agencies collect data for new policies such as new tax laws or population censuses, they require a statistical analysis plan outlining how the data will be collected, interpreted, and used. The SAP would specify the sample size, data sources, and statistical techniques that will be used to analyze the data and assess the effectiveness of the policy or program.

• Nonprofit Organizations

Nonprofits could also use SAPs to analyze data collected as part of a research study or program evaluation. A non-profit, for example, could gather information about who is likely to donate to their cause and how to contact them to solicit donations.

How Do You Write a Statistical Analysis Plan?

Here are the steps to writing a simple and effective Statistical analysis plan:

• Introduction

A statistical analysis plan (SAP) introduction should provide an overview of the research question or hypothesis being tested as well as the goals and objectives of the analysis. It should also provide some context for the topic and the context in which the analysis is being conducted.

• Methods

This section should describe how the data was collected and prepared for analysis, including sample size, data sources, and any analysis assumptions or limitations.

For example, a clinical trial involving 100 patients with a specific medical condition. The sample will be assigned at random to either the new or current standard treatment.

The SAP will include data on the treatment’s effectiveness in reducing symptoms, which will be collected at the start of the trial and at regular intervals throughout and after it. To avoid common survey bias, data is collected using standardized questionnaires created by researchers.

Next, the data will be cleaned and prepared for analysis by removing any missing or invalid values and ensuring that it is in the correct format. Also, any data collected outside of the specified time frame will be excluded from the analysis.

The small sample size and brief duration of the clinical trial are two of the study’s limitations. These constraints should be considered when interpreting the results of this analysis.

• Statistical Techniques

This section should describe the statistical techniques that will be used in the analysis, including any specific software or tools.

Using the preceding example, you can use software such as SPSS or R. They use t-tests and regression analysis to determine the effectiveness of the two treatments.

You can make further investigations using additional statistical techniques such as ANOVA. It enables you to investigate the effects of various variables on treatment efficacy and identify any significant inter-variable interactions.

• Results

This section describes how the results will be presented and interpreted, including any plans for visualizing the data or using statistical tests to determine their significance.

Using the clinical trial example, you can visualize the data and find patterns in the data by using graphical representations. Next, interpret the result in light of the research question or hypothesis, as well as any limitations or assumptions of the analysis.

Assess the implications of the clinical trial results and future research on the medical condition’s treatment. Then, develop a summary of the results including any recommendations or conclusions drawn from the research.

• Conclusion

The “Conclusion” section should provide a concise summary of the main findings of the analysis as well as any recommendations or implications. It should also highlight any limitations or assumptions of the analysis and discuss the implications of the results for clinical practice and future research. 

Information in the Statistical Analysis Plan

1. Statistics on who wrote the SAP, when it was approved, and who signed it.

2. Expected number of participants, and sample size calculation.

3. A detailed explanation of the main and short-term analysis techniques used for analyzing the data. This includes:

• Study goals
• Specify the primary and secondary hypotheses, as well as the parameters you’ll use to assess how well you met the study objectives.
• A detailed description of the study’s sample size.
• A summary of the primary and secondary outcomes of each study. Typically, there should be just one primary outcome.

4. The SAP should also specify how each outcome metric will be assessed. Statistical tests are typically used to examine outcome measures and the method for accounting for missing data.

5. The SAP should also explain the procedures used to analyze and display the study results in detail. This includes:

• The level of statistical significance that will be used, and if one-tailed or two-tailed tests will be used.
• How to deal with missing data.
• Outlier management techniques.
• Protocol variations, noncompliance, and withdrawal procedures.
• Estimation methods for points and intervals.
• How to calculate composite or derived variables, including data-driven definitions and any additional details needed to reduce uncertainties.
• Baseline and covariate data
• Add randomization factors
• Methods for dealing with data from multiple sources
• How to deal with participant interactions
• Multiple comparisons and subgroup analysis methods
• Interim or sequential analyses 
• Step-by-step procedure to terminate research and its implications
• Statistical software for analyzing the data
• Validate critical analysis assumptions and sensitivity analyses.
• Visual representation of the research data
• Define the safe population

6. Alternative models for data analysis if the data does not fit the chosen statistical model

Making Modifications to Statistical Analysis Plan

It is not unusual for a statistical analysis plan (SAP) to undergo adjustments during the project’s life cycle. Here’s why you may need to modify your SAP:

• Research question or hypothesis change: As the project progresses, the research question or hypothesis may evolve or change, requiring changes to the SAP.
• New data: As new data is collected or becomes available, it may be necessary to modify the SAP to include the new information.
• Unpredicted challenges: Unexpected challenges may arise during the project, requiring SAP alteration. For example, the data may not be of the expected quality, or the sample size may need to be adjusted.
• Improved Data Understanding: The researcher may gain a better understanding of the data as the analysis progresses and may need to modify the SAP to reflect this enhanced understanding.

Make sure to document the changes made to the SAP, as well as the reasons for them. This ensures the analysis’s reliability and accuracy.

You could also work with a statistician or research expert to ensure that the SAP changes are appropriate and do not jeopardize the results’ reliability and validity.

Conclusion

A statistical analysis plan (SAP) is a step-by-step plan that highlights the methods and techniques to be used in data analysis for a research project. SAPs ensure the reliability and validity of the results and provide a clear roadmap for the analysis.

You have to include the research question or hypothesis, sample size, data sources, statistical techniques, variables, and guidelines for interpreting and presenting the results to have an effective SAP.