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In our earlier topic on Better Understanding Continuous Distributions and Stochastic Modeling via the Normal Distribution, we got into some important and practical stuff on estimating population parameters. And then in Better Understanding Statistical Inference by Estimating the Mean and its Confidence Invervals we broadened our understanding of what it means to express the precision of an estimating parameter from standard errors (which imply a 68.26% level of confidence) to confidence intervals for any level of confidence we might want.

In this topic you will use what you learned in those topics to learn about something called hypothesis testing.

As you’ll see, hypothesis testing is another task of inferential statistics in which we test assertions or claims made about population parameters using sample statistics. And it’s super practical. In fact, we’re going to learn about it by looking at some pretty authentic examples you’ll run into in the geomatics context.


1. If you’re in my class, make sure you’re there for our discussion-based lectures.

2. Work your way through the lessons and self-assessments (under “Resource content”) below. Note that because we covered so many conceptual topics and examples in class, there are only applied problems for this topic.