About the author: Michael D. Lieberman has more than 29 years of experience as a researcher, statistician, and strategist in the marketing, advertising, and political research fields. He has worked extensively with clients in advertising attribution and advertising testing, data translation, financial services, information technology, food service, telecommunications, human resource, political polling, and public relations. He founded Multivariate Solutions in 1998 and now works with an international clientele including advertising firms, political strategy groups, and full service market research companies. Michael Lieberman is an Amazon featured author and has written more than 90 professional articles.

Machine learning gets a lot of buzz these days, usually in connection with big data and artificial intelligence (AI). But what exactly is it? Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering. The word learning in the term stems from the ability to learn from data. Machine learning is also widely used in data mining and predictive analytics, which some commentators loosely call big data.

Statistics is a huge field and many disciplines such as biology, economics and psychology have made significant contributions to it. This link to journals published by the American Statistical Association and this link regarding the popular statistical software R demonstrate just how big a field it is. Statistics is not just point-and-click and its growing complexity makes simplifying it more difficult, not easier. Moreover, in his popular textbook Statistical Rethinking, Richard McElreath of the Max Planck Institute makes a very important observation: "...statisticians do not in general exactly agree on how to analyze anything but the simplest of problems. The fact that statistical inference uses mathematics does not imply that there is only one reasonable or useful way to conduct an analysis. Engineering uses math as well but there are many ways to build a bridge."

What is a Time Series? Many data sets are cross-sectional and represent a single slice of time. However, we also have data collected over many periods - weekly sales data, for instance. This is an example of time series data. Time series analysis is a specialized branch of statistics used extensively in fields such as Econometrics and Operations Research.

At least once a week, I read or hear that new research has found that human memories are fallible and that, therefore, survey research and other "traditional" marketing research methods cannot be relied upon. Instead, we should use some new method that is being peddled. The new method may really be old wine in a new bottle. Usually these claims are wrapped in scientific or pseudo-scientific jargon and, on occasion, are made by academics. The problem is that the frailty of human memories is old news and well-known to professional marketing researchers and survey experts.

Quantitative research has been defined in various ways. Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to predict or explain a particular phenomenon. In marketing research, "quant" historically has meant consumer surveys. Analysis of consumer survey data has typically been limited to reporting numbers, perhaps broken down by age group, gender and a few other respondent groups of interest. The emphasis is mainly on the Who, What, When, Where, and How, though segmentation, conjoint, key driver and other analyses that delve into the Why are also occasionally conducted with consumer survey data.

Machine learning gets a lot of buzz these days, usually in connection with big data and artificial intelligence (AI). But what exactly is it? Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering. The word learning in the term stems from the ability to learn from data. Machine learning is also widely used in data mining and predictive analytics, which some commentators loosely call big data.

What is a Time Series? Many data sets are cross-sectional and represent a single slice of time. However, we also have data collected over many periods - weekly sales data, for instance. This is an example of time series data. Time series analysis is a specialized branch of statistics used extensively in fields such as Econometrics and Operations Research.

Many data sets are cross-sectional and represent a single slice of time. However, we also have data collected over many periods - weekly sales data, for instance. This is an example of time series data. Time series analysis is a specialized branch of statistics used extensively in fields such as Econometrics and Operations Research. Unfortunately, most Marketing Researchers and Data Scientists still have had little exposure to it.

Editor's note: Kevin Gray is president of Cannon Gray LLC, a marketing science and analytics consultancy. He would like to thank Marco Vriens of Ipsos for his helpful comments on a draft of this article. Machine learning gets a lot of buzz these days, usually in connection with big data and artificial intelligence (AI). But what exactly is it? Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering.