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How do you visually communicate confidence in data analysis?

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Data analysis is not only about crunching numbers, but also about communicating insights and recommendations. One of the key aspects of effective communication is to convey confidence in your data analysis, especially when you deal with uncertainty, variability, or error. How do you visually communicate confidence in data analysis? In this article, we will explore some data visualization techniques that can help you show your confidence level in a clear and compelling way.

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Chris French

Founder of DataFrenchy I Linked[in] Instructor I Sr. Analyst at Spring Health I Helping underpaid & unappreciated…

1 Confidence intervals

A confidence interval is a range of values that captures the true value of a parameter with a certain probability, based on a sample of data. For example, if you estimate the mean height of a population from a sample of 100 people, you can calculate a 95% confidence interval that means that you are 95% confident that the true mean height is within that range. Confidence intervals are useful to show the uncertainty and precision of your estimates, and to compare different groups or scenarios. You can visualize confidence intervals using error bars, box plots, or violin plots, depending on the type and distribution of your data.

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Chetan Rawal

Product Manager | Product Led Growth PLG Expert | ex-Upwork
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The audience is important when we use Confidence Intervals to tell a story. Because they can be tricky to interpret. More common amongst data analysts, they may become confusing very soon when you talk to business people like a customer support exec or a sales exec.

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2 Hypothesis testing

Hypothesis testing is a method of statistical inference that allows you to test whether a claim or assumption about a population is supported by the data. For example, if you want to test whether a new product has a higher conversion rate than the old one, you can set up a null hypothesis (no difference) and an alternative hypothesis (difference), and calculate a p-value that measures the probability of observing the data or more extreme results under the null hypothesis. A low p-value indicates that the data is unlikely to occur by chance under the null hypothesis, and suggests that you can reject the null hypothesis and accept the alternative hypothesis. You can visualize hypothesis testing using bar charts, line charts, or scatter plots, and annotate them with p-values, significance levels, or confidence levels.

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3 Sensitivity analysis

Sensitivity analysis is a technique that allows you to assess how the output of a model or analysis changes when you vary the input parameters or assumptions. For example, if you want to estimate the revenue of a business based on the price, demand, and cost of a product, you can perform a sensitivity analysis to see how the revenue changes when you change the price, demand, or cost within a certain range. Sensitivity analysis is useful to show the robustness and reliability of your results, and to identify the key drivers or factors that influence the output. You can visualize sensitivity analysis using tables, charts, or dashboards, and highlight the best-case and worst-case scenarios, or the optimal values for the input parameters.

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4 Data quality

Data quality is a measure of how accurate, complete, consistent, and relevant your data is for your analysis. Poor data quality can affect your confidence in your data analysis, as it can introduce errors, biases, or gaps that can distort your findings and conclusions. Therefore, it is important to check and improve your data quality before and during your analysis, and to communicate any limitations or issues that may affect your results. You can visualize data quality using histograms, frequency tables, or heat maps, and show the distribution, outliers, missing values, or inconsistencies in your data.

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Chris French

Founder of DataFrenchy I Linked[in] Instructor I Sr. Analyst at Spring Health I Helping underpaid & unappreciated professionals break into data analytics
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Use visual elements such as data quality indicators or symbols within your visualizations to highlight the quality of the data being presented. For example, you could use different colors or shapes to show data that is clean and verified along with data that might be uncertain or require further validation.

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5 Data storytelling

Data storytelling is the art and science of transforming your data analysis into a narrative that engages and informs your audience. Data storytelling is essential to communicate confidence in your data analysis, as it helps you to explain the context, purpose, and meaning of your data, to highlight the key insights and recommendations, and to persuade your audience to take action or make decisions based on your data. You can enhance your data storytelling by using visual elements, such as charts, graphs, icons, or images, that support and illustrate your message, and by using verbal elements, such as titles, captions, annotations, or summaries, that clarify and emphasize your points.

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Chris French

Founder of DataFrenchy I Linked[in] Instructor I Sr. Analyst at Spring Health I Helping underpaid & unappreciated professionals break into data analytics
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Make sure you have an engaging introduction, provide context, and gradually build up to your key insights and conclusions. Effective data storytelling is about having a balance of data visualization and storytelling.

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Chetan Rawal

Product Manager | Product Led Growth PLG Expert | ex-Upwork
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When it comes to "communication", Confidence Intervals are not the simplest way to use verbatim. E.g. what does a CEO make when you say There is a -10 to +5% improvement in our UX because of the last release. Point being, the confidence interval language is more common amongst the data analytics community versus heads of sales, customer support, etc.

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6 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?

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Chase Ochsner

Texas A&M Graduate Student | Labor Economics | Film Enthusiast | IWU Alumnus | PADI Open Water Diver
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One of my professors always reminded me to test for heteroskedasticity when running regression analyses. It's important to understand your dataset thoroughly so you can explain it in your research paper.

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How do you visually communicate confidence in data analysis?

1

2

3

4

5

6

1 Confidence intervals

2 Hypothesis testing

3 Sensitivity analysis

4 Data quality

5 Data storytelling

6 Here’s what else to consider

Data Analysis

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How do you visually communicate confidence in data analysis?

1

2

3

4

5

6

1 Confidence intervals

2 Hypothesis testing

3 Sensitivity analysis

4 Data quality

5 Data storytelling

6 Here’s what else to consider

Data Analysis

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