Explaining Confidence Intervals

Mala Mahadevan explains what confidence intervals are:

Suppose I look at a sampling of 100 americans who are asked if they approve of the job the supreme court is doing. Let us say for simplicity’s sake that the only two answers possible are yes or no. Out of 100, say 40% say yes. As an ordinary person, you would think 40% of people just approve. But a deeper answer would be – the true proportion of americans who approve of the job the supreme court is doing is between x% and y%.

How confident I am that it is?  About z%. (the common math used is 95%).  That is an answer that is more reflective of the uncertainty related to questioning people and taking the answers to be what is truly reflective of an opinion. The x and y values make up what is called a ‘confidence interval’.

Read the whole thing.

Related Posts

Principal Component Analysis With Faces

Mic at The Beginner Programmer shows us how to creepy PCA diagrams with human faces: PCA looks for a new the reference system to describe your data. This new reference system is designed in such a way to maximize the variance of the data across the new axis. The first principal component accounts for as […]

Read More

Using Uncertainty For Model Interpretation

Yoel Zeldes and Inbar Naor explain how uncertainty can help you understand your models better: One prominent example is that of high risk applications. Let’s say you’re building a model that helps doctors decide on the preferred treatment for patients. In this case we should not only care about the accuracy of the model, but […]

Read More

Categories

September 2017
MTWTFSS
« Aug Oct »
 123
45678910
11121314151617
18192021222324
252627282930