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What are the best ways to use experimentation to improve data journalism?

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Data journalism is the practice of using data, analysis, and visualization to tell stories and inform audiences. But how can you make sure your data journalism is accurate, relevant, and engaging? One way is to use experimentation, a method of testing different approaches, tools, and formats to find out what works best for your goals and audience. In this article, you will learn some of the best ways to use experimentation to improve your data journalism, from planning and designing your experiments, to measuring and learning from your results.

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Horacio K.

Institutional communication strategies. Private and government institutions and companies.

1 Plan your experiment

Before you start experimenting, you need to have a clear idea of what you want to achieve, how you will measure it, and what you will compare it to. A good way to plan your experiment is to use the SMART framework, which stands for Specific, Measurable, Achievable, Relevant, and Time-bound. This means you should define your experiment's objective, indicators, baseline, target, and duration. For example, you might want to increase the engagement of your data story by 10% in two weeks, by testing two different headlines and visualizations.

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Horacio K.

Institutional communication strategies. Private and government institutions and companies.
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Each element of the SMART framework can contribute to an effective and well-defined experiment: Clearly define the goal of your experiment. In your example, the goal is to increase engagement in data stories. What do you want to achieve, providing clarity and focus. Identify metrics that will allow you to quantify the success of your experiment. In this case, that's a 10% increase in engagement with data stories. Make sure your goal is realistic and achievable. Setting an achievable goal helps maintain motivation and prevents frustration. Make sure your experiment aligns with broader organizational goals and objectives. Limited in time: Set a specific time period for your experiment. In your example, the experiment lasts two weeks.

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2 Design your experiment

Once you have a plan, you need to design your experiment in a way that minimizes bias and maximizes validity. This means you should use a randomized and controlled approach, where you assign your participants to different groups or conditions, and expose them to different versions of your data journalism. You should also have a clear hypothesis, or a prediction of what you expect to happen, and a null hypothesis, or a statement that there is no effect or difference. For example, you might hypothesize that a headline that asks a question will generate more clicks than a headline that states a fact, and that a null hypothesis would be that there is no difference in click rates between the two headlines.

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Horacio K.

Institutional communication strategies. Private and government institutions and companies.
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Experiment design Aim: Increase engagement on data stories by 10% in two weeks by testing two different headlines and visualizations. Components: Independent variable Dependent variable Experimental setup: Participants (website visitors) will be randomly assigned to Group A or Group B. Each group will be exposed to a data story with a specific headline type. Hypothesis: Alternative hypothesis (H1): Null hypothesis (H0): Experiment duration Measurement Metrics: Click rates Time spent on the page. Data collection Control measures Ethical considerations Analysis Result

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3 Choose your tools

To run your experiment, you will need some tools that can help you collect, analyze, and visualize your data. Depending on your experiment's design and objective, you might use different tools for different purposes. For example, you might use Google Forms or SurveyMonkey to create online surveys, Google Analytics or Chartbeat to track website traffic and behavior, or Tableau or Datawrapper to create interactive charts and maps. You should also consider the ethical and legal implications of using these tools, and ensure that you respect the privacy and consent of your participants.

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Horacio K.

Institutional communication strategies. Private and government institutions and companies.
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Google analytics: Track website traffic, user behavior and engagement metrics. SurveyMonkey or Google Forms: Gather additional comments or ideas from participants Data analysis: Statistical analysis tools (e.g. R, Python with pandas and scipy): Data visualization: Tableau or data container: Create interactive, visually engaging charts and graphs. View engagement metrics and compare performance between the two types of headlines. Collaboration and communication tools (e.g. Slack, Microsoft Teams): Facilitate communication between team members involved in the experiment. Documentation tools (e.g. Google Docs, Notion): Maintain detailed records of experiment design, procedures, and results.

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4 Measure your results

After you run your experiment, you will need to measure your results and see if they support or reject your hypothesis. This means you will need to use some statistical methods to analyze your data and test its significance. For example, you might use a t-test to compare the means of two groups, or an ANOVA to compare the means of more than two groups. You should also use some descriptive statistics to summarize your data and show its distribution, such as mean, median, mode, standard deviation, or frequency. You should also use some visual methods to display your data and highlight its patterns, such as graphs, charts, or tables.

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5 Learn from your experiment

Finally, you will need to learn from your experiment and use your findings to improve your data journalism. This means you will need to interpret your results and explain what they mean for your objective and audience. You should also identify the limitations and challenges of your experiment, and how they might affect your conclusions. You should also suggest some implications and recommendations for your future data journalism, and how you can apply what you learned to other projects or contexts. For example, you might find that asking a question in your headline increased your engagement, but only for certain topics or demographics, and that you should test other factors, such as length, tone, or format, to optimize your data journalism.

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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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What are the best ways to use experimentation to improve data journalism?

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1 Plan your experiment

2 Design your experiment

3 Choose your tools

4 Measure your results

5 Learn from your experiment

6 Here’s what else to consider

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