In our earlier topic on Better Understanding Continuous Distributions and Stochastic Modeling via the Normal Distribution, we set the stage for / got half way to some important and practical stuff on estimating population parameters (e.g. when we used the normal case to understand what’s really happening when we talk about continuous distributions).
And even earlier than that, in our topic on Summarizing Data and Estimating Population Parameters Using Descriptive Measures, we got part of the way toward being able to estimate the precision of our estimated parameters (e.g. when we looked at the notion of a standard error).
This topic takes us deeper on both of those things. I want you to be able to estimate a population mean and, when also estimating its precision, get beyond the 68.26% confidence level implied by the standard error we’ve been using. Understanding this and really knowing what a confidence interval is opens a whole bunch of practical doors in engineering fields, including for geospatial applications.