Inductive Statistics

Inductive statistics (or inductive reasoning) is a branch of statistics that deals with taking samples from a larger population and using that data to:

• Draw conclusions,
• Make decisions,
• Forecast,
• Predict future behavior.

For example, you might want to know what if a new, mass-marketed food product is successful, based on consumers’ initial perceptions. As it isn’t possible to contact every consumer who purchased and tasted the product, you might conduct a taste testing for 100 consumers, and base your predictions on that small sample.

Inferential vs. Inductive Statistics

Inductive statistics and inferential statistics are really just two names for the same thing. In fact, many authors use the two terms interchangeably. For example:

• “Inferential statistics…is also called inductive reasoning or inductive statistics” (Jeneralczuk, 2011)
• “In inductive statistics probability theory is applied to make inferences about the process that generated the data” (Braune, n.d.)

However, there is a very subtle difference between the two terms. The name “inductive” comes from the term inductive reasoning, which is the process of drawing general conclusions based on specific information. Therefore, inductive statistics is the logical process of drawing general conclusions based on specific pieces of information; it is the underlying process behind inferential statistics, as opposed to the data (statistics) produced. In other words, the branch of inferential statistics (which includes estimation and hypothesis testing) uses inductive reasoning.

To put it another way, “inferential” is a wide brush that encompasses all the guesswork, hedging and estimates you can care to do with your small sample of data. On the other hand, “inductive” is a smaller part of that wide brush—the one used to draw general conclusions based on specific data.

References

Braune, C. (n.d.). Inductive Stats. Retrieved February 22, 2019 from: http://fuzzy.cs.ovgu.de/studium/ida/txt/ida_inductive.pdf
Jeneralczuk, J. (2011). The Three Main Aspects of Statistics. Article posted on website University of Massachusetts—Amherst. Retrieved February 22, 2019 from: http://people.math.umass.edu/~jeneral/stat240/handout1.pdf
Steen, K. Probability and Statistics, Chapter 2. Montefiore Institute. Retrieved February 27, 2018 from: http://www.montefiore.ulg.ac.be/~kvansteen/MATH0008-2/ac20112012/Class4/Chapter4_ac1112_v5a2.pdf

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Statistical concepts explained visually - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.