A skewed distribution in user research is a statistical pattern where data points are not symmetrically distributed around the mean, but instead are concentrated more on one side than the other, creating a "tail" in the distribution graph.
Synonyms: Asymmetric distribution, Non-normal distribution, Uneven data spread, Tailed distribution
In user research, a skewed distribution occurs when data collected from participants is not evenly distributed around the average (mean) value. This asymmetry can provide valuable insights into user behavior, preferences, or characteristics that might not be apparent in a normal distribution.
There are two main types of skewed distributions:
Understanding these types can help researchers interpret data more accurately and make informed decisions based on user patterns.
Recognizing skewed distributions is crucial in user research for several reasons:
When working with skewed distributions in user research:
What causes skewed distributions in user research?: Skewed distributions can result from various factors, including natural limitations (e.g., task completion times can't be negative), extreme user behaviors, or inherent characteristics of the data being collected.
How does a skewed distribution affect user research findings?: Skewed distributions can impact the interpretation of data, potentially leading to biased conclusions if not properly analyzed. They may reveal important subgroups or trends within the user population that require special attention.
Can skewed distributions be useful in user research?: Yes, skewed distributions can provide valuable insights into user behavior patterns, highlight areas for product improvement, and help identify niche user groups that might benefit from targeted features or designs.