And is sample size even the right question? I regularly conduct both qualitative and quantitative research. Regardless of qual or quant I'm often asked about sample size. An economist might balk at the idea that you can get value from 5–10 1 hour interviews. Yet there is a lot of value in qualitative research that can't be achieved with quant. And quantitative research has its own perils -- including sample size issues. Questions about sample size are more complex than they appear. A proper answer requires nuance. It depends on the theoretical justification for the results, the effect size observed, the number of hypothesis, and more.

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When Amazon launched in 1995, only 3% of Americans had ever been on the Internet, let alone purchased anything online. Both the concept of the web and e-commerce were startlingly new. Just a year earlier in fact, the New York Times had run a story with the headline: "Attention Shoppers: Internet Is Open", breathlessly reporting the first online transaction, the sale of a Sting CD. Also in 1995, Auction Web went live, which was to become eBay. A broken laser pointer – to a collector of broken laser pointers no less – for $14.83.

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."

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.

The words people use, the way they phrase things during conference calls, in reports or in regular conversations; all contain many of the quantitative keys that make Yin Luo such a highly respected analyst. "It's called natural language processing, using computer algorithms to, for example, analyze news stories" and draw conclusions, said Luo, vice chairman for quantitative research, economics and strategy at Wolfe Research, LLC. For example: A CEO has a conference call with analysts and says "I think" before offering insight. There is a distinct difference between beginning a sentence with "We think," which indicates a level of uncertainty seeking cover of the universal "we." "If they say'we think,' they are less certain," said Luo. This sort of analysis is the type of "alternative data" that excites Luo most about his job.