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This type of measurement is best used when the data has also been captured at the ordinal or ratio level as the orthogonality of the data set is reliable (Marshall, Ruiz, Bredillet, 2008). Extrapolating statistics to a broader population is also dependent on the approach of randomization used. When a solid methodology, sampling frame and approach to randomization have all been defined, inferential data is often used in organizations to define company-wide strategic initiatives. These include the decision to open more retail stores for a clothing or sporting goods retailer for example based on demographic data.
Inferential statistics are often used for experimental analysis where the statistical significance of relationships within the data is analyzed is highly useful for gaining insights into customer preferences and requirements (Ainslie, Leyland, 1992). Using inferential statistics to project the cause-and-effect relationships of marketing and sales programs has proven to have a high Return on Investment (ROI) as well (Marshall, Ruiz, Bredillet, 2008).
Conclusion
When data needs to be accurately and succinctly summarized for analysis and decision making, descriptive statistics are most often used. Conversely when data has been captured using statistically-sound methodologies with randomization as part of the sampling plan, extrapolations of results across...
The orthogonality of data in inferential statistics also is extremely useful for determining the magnitude of differences between groups, market segments and product attributes as perceived by customers (Ainslie, Leyland, 1992). Descriptive statistics are useful for the summarization of data that needs to be consolidated down to more understandable segments. Both of these approaches to statistics deliver the insights necessary in organizations to stay competitive. The use of descriptive statistics in managing the many functions of an organization, and the use of inferential statistics to interpolate survey results are both critical.
References
Ainslie, Andrew, & Pitt, Leyland. (1992). Customer retention analyses: An application of descriptive and inferential statistics in database marketing. Journal of Direct Marketing, 6(3), 31.
Robert A. Marshall, Philippe Ruiz, & Christophe N. Bredillet. (2008). Earned value management insights using inferential statistics. International Journal of Managing Projects in Business, 1(2), 288-294.
Basic Statistics: Tales of Distributions (2008) by Chris Spatz, Cengage Learning 9th ed. ISBN-13: 978049550218
Peter van den Besselaar. (2003). Descriptive statistics, inferential statistics, rhetorical statistics. Journal of the American Society for Information Science and Technology, 54(11), 1077.
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