Lauren Wang MKTG 211 002 Response 6

My biggest takeaway from this lecture was that given the number of times we use heuristics to make both everyday and large decisions, and how many errors we make when using heuristics, us as consumers must make many mistakes when using peripheral routes of processing.

For example, using frequency heuristics is often inaccurate and highly subjective based on the person and the information available to them. This is definitely apparent to me when making minor decisions such as where to eat for dinner. When thinking about risk, incidences that are more interesting or shocking to consumers are a lot more known and have a lot more prevalence in media, creating the availability bias that these incidences occur more than they do. This could be hazardous for risks that are less “sexy” and that people do not think a lot about. For example, in the example of estimates of deaths from accident vs. diabetes, the actual result where diabetes is four times more likely to occur than an accident is frightening. Consumers should be more cautious of preventing diabetes. Without this awareness due to selective media coverage, consumers are focusing on the wrong things that could be detrimental to their health or wellbeing.

I have learnt about the law of large numbers from my statistics classes, but learning about the law of small numbers showed me how people tend to exaggerate the degree to which a small sample will resemble the population. This reminds me of the availability bias where estimates for the population are based on estimates from available knowledge of small samples. We have huge assumptions about populations based on small and unrepresentative knowledge or experiences. It was also interesting learning about Gambler’s Fallacy from a marketing perspective after learning about it in my decision possesses class.

Overinference from small numbers showed me how large impressions and decisions may be founded on just cyclical fluctuation. Something as substantial as punishment and reward such as bonuses may not simple be due to performance, but on underappreciating of regression to the mean.