Sampling Research: Cure Coffeehouse And Brassie

Insights gained from sampling research can provide strong guidance for a business, but one must always examine the accuracy of the sample through its size, level of confidence, and its margin of error. Just as good sampling data can help a company, acting on false information can be detrimental for a business. This paper aims to examine the sampling research, its accuracy, and how it can relate to Cure Coffeehouse and Brassie, an independent coffeehouse in Norfolk, VA.

Cure coffeehouse is an upscale coffee house based out of Norfolk, Virginia. They define themselves as a “relaxing coffeehouse & cafe specializing in espresso drinks, craft beer, sandwiches & charcuterie” (Google, n.d.). While they may be the local’s favorite for getting

According to Michael Luca of Harvard Business School (as citied in Simonson & Rosen, 2014), informational services, such as online reviews or word of mouth, are eclipsing traditional marketing in many major cities. Word-of-mouth, both in-person or online, comes from the emotional responses related to a customer’s experience (Jalilvand, Salimipour, Elyasi, & Mohammadi, 2016). This can be a very effective tool for marketers because a word-of-mouth account is considered to be far more credible the eyes of other customers. The emotionally charged account is also valuable to researchers because it holds valuable data that is both quantitative and qualitative; the number of ‘stars’ provides quantifiable data, while the reviews themselves hold quality information. This makes online reviews extremely useful for businesses like Cure Coffeehouse, and the restaurant industry as whole, because it serves as most restaurants’ primary marketing method (Jalilvand, et al.,

Sampling is a process where research is gathered from a small subset of a larger population in order to information about the population at large (McDaniel, 2013). Since surveying an entire population is often not a realistic option, sampling helps researchers assess a population without having to poll everyone within that population. However, sampling is not 100% accurate every time; the smaller a sample size gets, the larger the chance for error and the harder it is to know whether a perceived difference is real or due those errors (McDaniel, 2013). For this reason, sampling data includes a confidence level, which is the probability that a sample represents the true value of the population. Because confidence levels may vary, researchers are able to select their desired level of confidence depending on what is being sampled and their need for