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Normal Distribution and Its Application

In: Statistics and Research Methods in Psychology with Excel

Author

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  • J. P. Verma

    (Lakshmibai National Institute of Physical Education, Department of Sport Psychology)

Abstract

All parametric tests are based on normality; hence, it is essential to understand the normal distribution and its characteristics. Skewness and kurtosis are the two benchmarks for understanding distribution of data. Using these characteristics, one can describe the nature of research data and draw meaningful conclusions. For instance, negatively skewed distribution conveys that most of the scores are above the mean, and similarly, positively skewed distribution reveals that majority of the data lies below the mean. In skewed data, one should report median and quartile deviation instead of mean and standard deviation. Normal distribution is said to be mesokurtic, whereas distribution having more peaked is leptokurtic and less peaked is platykurtic. Leptokurtic distribution reveals less variation in the data around its mean, whereas platykurtic distribution scores are widely spread around its mean. This chapter discusses the normal distribution and its application by using several examples in detail.

Suggested Citation

  • J. P. Verma, 2019. "Normal Distribution and Its Application," Springer Books, in: Statistics and Research Methods in Psychology with Excel, chapter 0, pages 201-235, Springer.
    • Handle: RePEc:spr:sprchp:978-981-13-3429-0_6
    DOI: 10.1007/978-981-13-3429-0_6
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