How do you communicate and visualize the uncertainty and variability of your data sample results?

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Data sampling is a common technique in data analytics to draw conclusions from a large population by analyzing a smaller subset. However, data samples are subject to uncertainty and variability, which means that the results may not be exactly the same as the true population parameters. How do you communicate and visualize the uncertainty and variability of your data sample results? In this article, you will learn some best practices and tips to do so effectively and accurately.

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Naiya Patel, PhD, MPH

Public Health Sustainability scientist and Social entrepreneur leading innovation in Corporate Social Responsibility…

1 Why is uncertainty and variability important?

Uncertainty and variability are two aspects of data quality that affect the validity and reliability of your data sample results. Uncertainty refers to the degree of confidence or doubt that you have about your results, while variability refers to the degree of dispersion or diversity that your results show. Both uncertainty and variability depend on factors such as the sample size, the sampling method, the measurement error, and the population characteristics. Ignoring or misrepresenting uncertainty and variability can lead to misleading or inaccurate conclusions and decisions.

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2 How do you measure uncertainty and variability?

One of the most common ways to measure uncertainty and variability is to use confidence intervals. A confidence interval is a range of values that contains the true population parameter with a certain level of confidence, usually 95% or 99%. For example, if you estimate that the mean income of a sample of 100 households is $50,000 with a 95% confidence interval of $45,000 to $55,000, it means that you are 95% confident that the true mean income of the population is between $45,000 and $55,000. The width of the confidence interval reflects the variability of your data sample, as well as the level of confidence that you choose. A narrower confidence interval indicates less variability and more precision, while a wider confidence interval indicates more variability and less precision.

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3 How do you communicate uncertainty and variability?

When you communicate your data sample results, you should always report the confidence interval along with the point estimate, such as the mean, the median, or the proportion. This way, you can convey the uncertainty and variability of your results in a transparent and honest way. You should also explain what the confidence interval means and how it was calculated, and avoid using terms such as "error" or "margin of error" that may confuse or mislead your audience. Instead, use terms such as "uncertainty" or "range" that are more accurate and neutral.

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Naiya Patel, PhD, MPH

Public Health Sustainability scientist and Social entrepreneur leading innovation in Corporate Social Responsibility (CSR)
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While providing Confidence Intervals is one of the ways to communicate uncertainty and variability in data sample, there are other measures one can take. In my work, I clearly explain through text and flow diagram what data sampling methods and steps were taken, data inclusion/exclusion criteria with reasons, number of people excluded at each stage, descriptive statistics of excluded sample, and other similar ways. It will allow to measure variability of point estimates with reproducibility, and transparency. I would also create study sample and sub-sample to run same analysis and compare the results in terms of variability/uncertainty. If the results do not differ, unadjusted confounders in study context are identified one can rely on it.

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4 How do you visualize uncertainty and variability?

Visualizing uncertainty and variability can help you and your audience to better understand and interpret your data sample results. Different types of charts and graphs can be used to visualize uncertainty and variability, depending on the data type and message. Error bars are vertical or horizontal lines that extend from the point estimate to show the confidence interval, while box plots show the median, quartiles, and outliers of your data sample. Shaded regions are areas that show the confidence interval around the point estimate, often with a lighter color or lower opacity. Dot plots display each individual observation or value of your data sample as a dot, useful for showing diversity and frequency, as well as highlighting outliers or clusters.

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5 What are some best practices and tips?

When communicating and visualizing uncertainty and variability, it’s important to follow best practices and tips to ensure that your message is clear, accurate, and effective. To start, select the appropriate level of confidence for your confidence interval - usually 95%, though you may need to adjust it depending on your context and purpose. Additionally, choose the appropriate chart or graph for your data type and message, adding labels, legends, titles, and captions to explain it. Utilize consistent and meaningful colors, shapes, and symbols to represent uncertainty and variability, as well as descriptive and precise language to describe them. Moreover, provide context and interpretation for uncertainty and variability to avoid presenting them as limitations or weaknesses.

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How do you communicate and visualize the uncertainty and variability of your data sample results?

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1 Why is uncertainty and variability important?

2 How do you measure uncertainty and variability?

3 How do you communicate uncertainty and variability?

4 How do you visualize uncertainty and variability?

5 What are some best practices and tips?

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