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Skewed distribution Definition

A distribution whose frequency curve has one tail longer than the other - not symmetrical about its mean. If the left tail is longer than the right, it is called skewed left.

Skewed distribution refers to the uneven spread of data points in a dataset. In this distribution, most of the data clusters toward one end, while the other end has fewer data points. This results in a long tail extending towards the less frequent values. Skewed distributions can be positively skewed (skewed to the right) or negatively skewed (skewed to the left).

Who relies on skewed distribution in market research?

Market researchers, data analysts and statisticians rely on understanding skewed distributions to accurately interpret and analyze data. When studying consumer behaviors, purchasing patterns or preferences, recognizing skewed distributions helps in identifying trends and outliers that can impact decision-making.

Why should I care about skewed distribution in market research?

Recognizing and addressing skewed distributions is important because they can influence the conclusions drawn from data analysis. Skewness affects the mean, median and other statistical measures, potentially leading to biased insights. Understanding this concept helps ensure that your interpretations are based on a thorough understanding of your data.

Why is skewed distribution important in market research?

• Skewed distributions can provide valuable insights into consumer behavior and preferences.
• By identifying whether data is skewed and in which direction, you can make informed decisions about which statistical measures to use and how to interpret trends.
• This understanding leads to more accurate market insights, better-targeted strategies and improved decision-making.