Why are confidence intervals useful?
Confidence intervals give users a sense of the accuracy of an estimate. Generally speaking, the wider an interval, the poorer the estimate since you are trying to learn the mean or true value of a data set.
It also gives researchers a way of expressing findings from a sample of a population since the purpose of taking a sample is to learn more about the general population it represents.
In finance and business, there are a number of applications for a confidence interval. You could use it in market research, for example, to determine how much of a given product you might sell over a given time period. Similarly, you could use a confidence interval in risk management as well. Businesses often use fuel or foreign currency hedging to protect against price fluctuations. Confidence intervals can help inform those decisions.
How to use confidence intervals
It’s important to understand that a confidence interval doesn’t reflect a complete data set. As a hypothetical example, you might say that, based on past history, there is a 90% chance that the stock market will move 30% or less in a given year.
Gathering information from a known data set using a normal distribution (also known as a bell curve), the mean, and standard deviation is different from coming up with a confidence interval, which is based on an incomplete data set and comes from a sample rather than an entire population.
Political polls, for example, are based on a sample of a population and come within a margin of error, which expresses a similar concept as confidence intervals. The pollsters have a certain level of confidence that the actual percentage of voters who prefer a particular candidate falls in the range that includes the margin of error.
The Census Bureau uses an example of the number of Americans in poverty in 1996, estimating with a 90% confidence interval of 35,534,124 to 37,315,094.
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