Monthly Archives: November 2015
Sampling Bias
This video covers the particular sources of bias which cause a sample to either not be as representative as one would hope, or to cause the data yielded to not accurately reflect the sample.
Sampling Error vs Nonsampling Error vs Nonrandom Sampling Error
The title says it all, even if you spell it non-sampling and non-random, with a dash in there.
In statistics, you're taking a sample in order to find something out about the population. These videos cover the various ways that either a sample is not representative of the population, or the sample itself is representative yet the data you get from the sample isn't accurate to the sample (thus not to the population either). Random sampling error, Nonrandom sampling error (non-random), Nonsampling error (non-sampling).
Levels of Measurement (Ratio, Interval, Ordinal, Nominal)
These don't make a whole lot of sense when you first learn them, but after this video you'll hopefully see that if you understand the ratio level first, the others fall into place.
This chapter covers the "levels of measurement" -- ratio, interval, ordinal and nominal. They don't make a whole lot of sense when you first learn them, but after this video you'll hopefully see that if you understand the ratio level first, the others fall into place.
Systematic Sampling
Random sampling sounds good on paper, and in medical studies. But in the real world, it's way easier to use systematic sampling to grab a sample that's almost as good as random.
Stratified vs Cluster Sampling
Stratified vs cluster sampling is a common confusion, so that's why I made sure to put them in the same video to confuse you further. Scratch that, I meant "clearly explain the difference". You knew what I meant.
Sampling With or Without Replacement
If you're in the habit of deriving and proving statistical formulas in your spare time, then you're definitely going to want to watch someone else's replacement video, since I'm basically going the other direction with it. "With replacement" is the boilerplate of intro stats!
Convenience Sampling
This is one of those rare math terms where they tell you what it is just so they can tell you not to do it. What example did I pick to illustrate this point? A bunch of science projects from my kids' elementary school! Enjoy.
Simple Random Sampling
Different from plain-old-normal random sampling (in ways that don't really matter). This video puts the simple in simple random sampling.
Lots of sampling methods: simple random sampling, convenience sampling, sampling with and without replacement, stratified vs cluster sampling, and systematic sampling!
Double-Blind, Placebo-Controlled Study
This term mostly applies only to medical research trials, such as investigating new drugs and whatnot, but it's illustrative to show you just how awesomely statistical a study can be. No confounding variables here!
Prospective (Longitudinal or Cohort) vs Cross-Sectional vs Retrospective Studies
With these studies, it's mostly a matter of timing. Discuss.
Observational Study vs Designed Experiments
This video may be, at first glance, about observational and designed experiments. But really it's a how-to for your future career as a data-mastering statistics master who will not -- who dare not -- do experiments that suck (statistically speaking). God speed.
Observational study, designed experiment, prospective (longitudinal or cohort) vs cross-sectional vs retrospective studies, double-blind, placebo-controlled study.
Confounding & Lurking Variables
"Confounding" and "lurking" are the words statistics people use to say "oops". As in, "oops, I didn't account for that variable." Stay tuned for a couple examples and the one tried and tested way to avoid these "oops" in your own experiments. (You're doing experiments, right?)
Explanatory & Response Variables
Like x and y in algebra, explanatory and response variables are the yin and the yang of studies in statistics.
This chapter covers the main types of variables you'll see in stats: explanatory variables, response variables, lurking variables, and confounding variables.
Census vs Sample
To census or sample? That is the question. It's a subtle distinction, but like so many small things in stats, you may see it in a short answer question on a test.