It’s Just Like Driving a Car:
It’s Just Like Driving a Car:
In Statistics we spend a lot of time working towards inferential statistics, in particular Confidence Intervals. Inferential Statistics is the idea that most people have about statistics; that is if you want to know something about a population then you take a sample of that population and infer from that sample what the whole population might want, or be, or do etc. For example, taking a sample of shoppers and how much they spend at a certain store. From the sample we can infer the amount of money a shopper would spend on average at a store. Then we can infer from that sample that this is the average amount spent by ALL shoppers at the store. However, we must also account for errors in sample so we add some amount of money error and we subtract some amount of money error. This how we construct a Confidence Interval.
As you can see there are already a lot of moving parts and it always seems to me that this idea of taking a sample and extrapolating to the population should be an obvious concept to marketers. But I have to be a careful with this blind spot and break things down into their component parts. In order to master the creation of Confidence Intervals the student must first understand and master means. Just finding the average of a population. Then they must understand that if you take many samples of the same size and create a distribution you will have a Sampling Distribution. Then the students must master the Central Limit Theorem as it applies to Normally Distributed Sampling Distributions and then final they must master the concept of Sampling Error and how we account for this when creating a Confidence Interval. This is why we spend almost 9 weeks before we get to Confidence Intervals. Building the steps and the mastery to get to the point of Inferential Statistics and Confidence Intervals.
TweetExample for "It’s Just Like Driving a Car:":
https://psu.instructure.com/courses/1844486/files/81532530/download?verifier=NZvwBItgGtQvLKZKcmQZSce1hTkOecKheSyGl9WL&wrap=1
Leave a Reply