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Skewed Distribution

What is Skewed Distribution?

A skewed distribution is characterized by its lack of symmetry around its center point. Typically, data in a skewed distribution clusters more heavily towards one end rather than being evenly distributed. Time-on-task data frequently exhibits skewness. Unlike normal distributions, skewed distributions are not symmetrical.

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Why is Skewed Distribution Important?

  • Recognizing Data Patterns: Understanding skewed distributions helps researchers recognize non-normal patterns in data, allowing for appropriate statistical analysis and interpretation.
  • Identifying Data Trends: Skewed distributions indicate where data clusters, providing insights into common behaviors or occurrences within a dataset.
  • Applying Correct Analysis Techniques: Skewed distributions require specialized analytical approaches, ensuring accurate modeling and interpretation of data.

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Moderated Usability Testing 101

Moderated Usability Testing 101

UX researchers can often be at a loss to understand what users may be thinking! However, moderated usability testing engages participants in real-time, allowing them to vocalize and discuss their actions and thoughts. This direct approach can uncover deep and insightful information about why, how, and what users think.

Standard Deviation