Introduction
When presenting data in dashboards, especially from surveys or research studies, the sample size is a critical metric that provides context for the results. A larger sample size generally indicates more reliable estimates and allows for better generalization to the larger population. For instance, a study with a sample size of 1,000 is likely to yield more accurate insights than one with just 50 participants, especially in diverse populations.
These sample sizes can best be presented by incorporating them into visualizations. This can be done by labelling the sample sizes clearly (e.g., n =400) on the charts and graphs to inform viewers about the number of responses or observations represented.
(In the above view, users can easily see the sample sizes as displayed on the chart title. Enabling them to interpret results based on the context of analysis)
Note: Applying the filters to the view automatically changes the sample size. For example, selecting Male respondents with Masters or PHD degree changes the sample size to 89 – availing this information to users helps them interpret results based on the context.
Computing the sample size
So, how do you compute the sample size in Tableau.
You compute the sample size by calculating the total number of unique respondents as shown in the calculation below.
(If your dataset, doesn’t have a Respondent ID, use the field that represents each respondent to compute the sample size)
Presenting the sample size
To present the sample size, drag the calculation ‘Sample size’ to the detail shelf. Ensure the calculation is computed using the question being analysed. In this case, “Q3e”.
To display the sample size,
Go to edit title.
Customize the title and insert the sample size from the drop-down menu.
Executing this adds the sample size on the chart title. Note, this calculation is dynamic – meaning the sample size will change based on the items selected on the filters card.
Conclusion
Incorporating sample size considerations into dashboards is essential for presenting credible and actionable insights. It enhances representativeness, ensures statistical validity, and fosters transparency regarding data quality and reliability. By prioritizing appropriate sample sizes, organizations can improve decision-making processes based on their dashboard analytics.
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