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How To Lie With Numbers
It takes less effort to lie without numbers, but there are now more numbers and more ways to lie with them than ever before. Poor Reverend Bayes, who understood the true meaning of "evidence".
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Vital Statistics You Never Learned… Because They’re Never Taught
Marketing scientist Kevin Gray asks Professor Frank Harrell about some important things we often get wrong about statistics.
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Sampling: A Primer
Though it doesn’t get a lot of buzz, sampling is fundamental to any field of science. Marketing scientist Kevin Gray asks Dr. Stas Kolenikov, Senior Scientist at Abt Associates, what marketing researchers and data scientists most need to know about it.
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Marketing Analytics for Data Rich Environments
A lot is changing in the world of marketing analytics. Marketing scientist Kevin Gray asks Professor Michel Wedel, a leading authority on this topic from the Robert H. Smith School of Business at the University of Maryland, what marketing researchers and data scientists most need to know about it.
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 Making Sense of Machine Learning
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.
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Qualitative Research Methods for Data Science?
Why on Earth would a data scientist need to know about qualitative research? There are plenty of reasons. Here are a few.
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Propensity Scores: A Primer
Propensity scores are used in quasi-experimental and non-experimental research when the researcher must make causal inferences, for example, that exposure to a chemical increases the risk of cancer.
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What Makes a Good Analyst?
Without doubt, critical thinking is necessary in order to be a good analyst but particular skills and experience are also required. What are some of these skills?
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Stuff Happens: A Statistical Guide to the “Impossible”
Why are some people struck by lightning multiple times or, more encouragingly, how could anyone possibly win the lottery more than once? The odds against these sorts of things are enormous.
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 What is Structural Equation Modeling?
Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. What is its relevance to Marketing Research?
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Making Sense of Machine Learning
What is Structural Equation Modeling?