Natalie is a teacher and holds an MA in English Education and is in progress on her PhD in psychology.
Ginny owns a pet store. Currently, she stocks five types of dog food, but she's considering stocking two more types. She thinks that could result in more sales because people could find the dog food they really want to feed their dog. On the other hand, she could pay lots of money to stock up on the different types of dog food and then discover that her customers don't really want the new stuff. Then she'd be out a lot of money, and she'd be stuck with stock of dog food that she can't sell!
Business leaders face decisions every day. Like Ginny's, these choices often come with both the possibility of a positive outcome and the possibility of a negative outcome. So, how do companies make their decisions?
One way to make business decisions is by using probability, or the mathematical chance that something will turn out a certain way. For example, the two new dog food companies that Ginny is considering both provided her with data on how much of their products other pet stores sold. Based on this data, Ginny calculated that there is a 60% probability that she will sell more dog food if she stocks them and a 40% probability that she'll be stuck with unwanted stock.
That sounds like a pretty good start for Ginny. But, what does she do if new information comes in? For example, what if she discovers that the data was for all pet stores in the country but that pet stores in her region have a different set of data? To help Ginny out, let's look at how to incorporate new information and revised probability into the decision-making process.
The best possible scenario for Ginny would be that she knows for sure whether or not the new dog food brands will sell. But, there's no way to know in advance exactly what is going to happen or what the outcome of a decision will be. As we've seen, people like Ginny often use probability to help them make decisions. But often new information comes in that changes the probabilities. What then?
In general, the more information available, the better the decision. This is because additional information will help Ginny understand and predict outcomes better. For example, at first, she had data that was from pet stores all across the country. That allows her to calculate probability better than having no data or incomplete data.
But not all information is created equally, so relevant information is better than irrelevant information. For example, Ginny now has data that covers her region of the country. That's better than the information that covers the entire nation. But if instead she had data that looked at a different region, that might not be better (and could be worse) than focusing on the national data.
Finally, good information is better than unreliable or bad information. Take Ginny's data again: the national data was provided by the dog food companies, who have a motivation to make the numbers appear better than they actually are. Suppose that Ginny had the opportunity to look at data that was collected from a third party, like a government entity. That could be better, more reliable data.
Ok, Ginny gets that additional data is best, if it is relevant and good or reliable. But if she gets new information, what does she do with it? How does it affect her probabilities?
Before we answer that, let's head to the craps table for a minute. We're rolling dice, and after 20 rolls of the dice, we realize that there is a 17% probability that one or both of the dice will come up with a four. So, our initial information tells us that the probability of rolling a four is .17.
But let's say that we keep playing, and as we gather more information, we realize that something is happening. One of the die is rolling a four more than we would expect, based on the 17% probability. In fact, after 100 rolls of the dice, we realize that the probability of rolling a four is 28%, or .28, not 17%.
Now, besides suspecting that one of the dice is loaded, we might want to change our bets to reflect the new probability. Calculating probability based on new information produces a revised probability. Revised probability allows people to calculate probability based on new information without starting all over again from scratch. So instead of just throwing out the data we collected on the first 20 rolls, we can use revised probability to include new data and our original data to figure out a revised probability.
What does this mean for Ginny? Well, she has two options: she can throw out the national data set that she has and just focus on the regional data. Or she can combine the two and use revised probability to figure out what her chances are based on both data sets.
Now, if Ginny has data that reflects her specific region, she might want to just use the new data. But let's say that she has information that is from a region that's similar to hers but not exactly the same. In that case, she might want to use revised probability to change the national probability and help her get a better picture of what her chances of selling lots of dog food are.
Business leaders face decisions every day. One way to make these decisions is by using probability, or the mathematical chance that things will turn out a specific way. When using probabilities, sometimes new information comes into play. In general, the more information available, the better the decision. But not all information is created equally, so relevant information is better than irrelevant information. In addition, good information is better than unreliable or bad information.
Calculating probability based on new information produces a revised probability. Revised probability allows people to calculate probability based on new information without starting all over again from scratch. Business leaders have two options when faced with new information: throw out the original data to start over or combine the old and new data into a revised probability.
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