How can you create a representative data visualization using statistical sampling?

1 Choose your sampling method

When selecting a sample from a data set, the research question, data availability, and desired accuracy should be taken into consideration. Random sampling is a popular method that involves randomly selecting data points from the population without any bias or preference. Stratified sampling divides the population into groups or strata based on some relevant variable, and selects data points proportionally from each group. Cluster sampling divides the population into clusters or segments based on some geographic or demographic criteria, and selects data points from a few randomly chosen clusters. Systematic sampling involves selecting data points at regular intervals from a sorted list of the population. Each sampling method has its own advantages and disadvantages; therefore, it is important to choose the one that best suits your data visualization goal and data quality.

2 Determine your sample size

The sample size is the number of data points you need to select from the population to create a representative data visualization. This size depends on several factors, such as the level of confidence you want to have in your results, expressed as a percentage or a z-score, and the margin of error you are willing to accept, expressed as a percentage or a standard deviation. Additionally, the variability or standard deviation of the population and the design effect or adjustment factor for complex sampling methods should be taken into account. To determine the sample size for different sampling methods and scenarios, there are various formulas and calculators available. It is important to use the appropriate one for your data visualization project and verify the assumptions and limitations of each formula.

3 Collect and analyze your sample data

Once you have chosen your sampling method and determined your sample size, you can proceed to collect and analyze your sample data. Depending on the data source and availability, you can use random number generators or tables to select random data points from the population, as well as data analysis software or programming languages such as Excel, R, Python, or SQL to apply filters, queries, or functions based on certain criteria or rules. Additionally, data visualization software or libraries like Tableau, Power BI, Matplotlib, or ggplot2 can be used to import, explore, and visualize your sample data. Before creating your data visualization, it's important to check the quality and validity of your sample data. Descriptive statistics like mean, median, mode, range, or standard deviation can be used to summarize and compare your sample data with the population data. Inferential statistics such as confidence intervals, hypothesis tests, or error bars are also useful for estimating and communicating the uncertainty and variability of your sample data.

4 Design and create your data visualization

The final step is to design and create your data visualization using your sample data. To ensure it is effective, you should follow data visualization design principles and best practices such as choosing the right type of chart or graph for your data and message, utilizing appropriate colors, shapes, sizes, labels, legends, and titles to enhance the readability and aesthetics of your data visualization, avoiding misleading or confusing elements, and providing context and explanation for your data visualization. Additionally, you should evaluate and refine your data visualization based on the feedback and reactions of your intended audience by using different methods and metrics such as surveys, interviews, eye-tracking, or click-through rates. By doing so, you can create a representative data visualization using statistical sampling that can help you communicate your data insights clearly and convincingly.

5 Here’s what else to consider

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