In Statistics, "population" and "individual" can refer to people, things, dogs... anything you have data for. In this data we'll discuss the ins and outs of these terms, as well as what to look for in test questions.
Descriptive vs Inferential Statistics
You may not need to know these terms for a test -- but then again you might, depending on your teacher. Either way, this video introduces you to the concept of why you're taking a stats class in the first place, and how stats applies to any field of business, science, or marketing.
Parameter vs Statistic
A minor vocabulary nuisance, this video explains the difference between parameters and statistics, and how that difference will infuse your stats class with the appropriate level of statisitcal inference.
Quantitative vs Qualitative (Categorical) Data
The basic definition of quantitative data is that it is described by numbers, whereas qualitative data is everything else; however, in practice your teacher can bring up some tricky gray areas where numbers are in fact categorical. We'll prep you for that in this intriguing video!
Continuous vs Discrete
Whether you're talking about variables or data, continuous and discrete describe the same situation: is the data something you count, or are decimals okay? And what about those tricky situations that my teacher seems to find so interesting, where something discrete is actually considered continuous?
The Normal Distribution & z-score
You're going to be hearing the world "normal" every two minutes for the rest of the semester, in tons of depth, so this video isn't going to do anything but scratch the surface of this topic. But if you're wondering what the heck this normal thing is, and what z-scores have to with anything, this video introduces them both.
There are lots of ways to define what an outlier is, other than a "way out there" piece of data you want to just ignore to make your homework easier. This video explains what outliers are, and the most common way of defining them.
Reliability vs Validity
Kind of like "accuracy vs precision", reliability and validity are the terms for whether a test is both repeatable and accurate.
Variability & Dispersion
These are two names for the same thing: how spread out is the distribution. It's all subjective, though, with lots of ins and outs, so naturally we'll delve into examples involving small dogs to explain what's happening here.
Statistical Significance & Confidence Intervals
The last 3/4 of your typical stats class -- or more -- is about determining whether a sample or test is statistically significant. In other words, does the data from your survey/study/experiment mean something, or was it just luck, kind of like flipping a coin and getting 5 heads in a row?
Correlation vs Causation
This video introduces one of the biggest problem plaguing big, real-world problems: just because two things seem to go together, what if it's just a coincidence? Does carbon dioxide cause global warming, or is it just a coincidence that a historic rise in greenhouse gasses corresponds to a historic rise in global temperatures? That's correlation vs causation.
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.
If you do not have an account, you should get one, because it is awesome! You can save a playlist for each test or each chapter, and save your "greatest hits" into a "watch right before the final" list (not that we recommend cramming, but when in Rome...)