Introduction to Principles of Statistics for Data Analysis
Statistical principles provide a practical foundation for understanding data. Basic methods such as counts, averages, percentages, and measures of variation help turn raw values into useful information, making it easier to identify patterns, compare results, and support workplace decisions.
Technique Overview
Introduction to Principles of Statistics for Data Analysis Definition
Statistical principles are the basic concepts used to summarise, compare, and interpret data. They include counts, frequencies, percentages, measures of central tendency such as mean, median, and mode, and measures of variation such as range and standard deviation. These principles help identify patterns and differences while supporting accurate interpretation of data within its context (Freedman, Pisani and Purves, 2007; Diez, Barr and Çetinkaya-Rundel, 2019).
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Further Reading
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Introduction to Principles of Statistics for Data Analysis references (4 of up to 20) *
- De Veaux, R.D., Velleman, P.F. and Bock, D.E. (2018) Stats: Data and Models. 5th edn. Harlow: Pearson.
- Diez, D.M., Barr, C.D. and Çetinkaya-Rundel, M. (2019) OpenIntro Statistics. 4th edn
- Freedman, D., Pisani, R. and Purves, R. (2007) Statistics. 4th edn. New York: W.W. Norton & Company.
- Moore, D.S., McCabe, G.P. and Craig, B.A. (2017) Introduction to the Practice of Statistics. 9th edn. New York: W.H. Freeman and Company.
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