This is an area where I think the usual proper math approach of abstract patterns and theorems and proofs is all wrong for the general public (the ones in a general public school).
They should teach it from the perspective of what problems you are trying to solve, what your options are, and how those options work in practice.
The "in practice" part should be lots of simulations with something like a ChromeBook, so people could really see the ideas working. Create a (simulated) bucket with 70% red balls and 30% blue, then pull samples of various sizes and see how well the sample means match what you already know is the "truth" (70/30). You'll soon have everybody seeing that bigger samples aren't always better, but they tend to be, and they'll see how much better they get as a function of size. Make charts of the improvement. Talk about how good the estimate really NEEDS to be.
Then try some tests where the students DON'T know the "truth" about the population and have to use samples to estimate it. How far off do they think they are. How confident about various intervals, etc.
Then start explaining some of the math that lead to formulas that let you calculate these estimations, sample sizes, confidence intervals, etc.
Have estimation games: you get points for getting an estimate within some delta of the population mean, but you have to pay points to buy your samples. So, how big a sample does your team want to buy?
Then continue with other basic prob/stat ideas such as bayesian cancer tests (does this mean you have cancer or not?) and courtroom dramas (does this mean he's probably guilty or not?), commonly misunderstood situations (ex: Simpson's Paradox), how Gaussian estimators begin to fail when things aren't independent, and all sorts of other introductions to quantitative thinking about real life scenarios.