Name: Crash Course: Statistics
Uploaded: 2018
Description: In Crash Course Statistics, we'll take a closer look at how statistics play a significant role in our everyday lives..

In Crash Course Statistics, we'll take a closer look at how statistics play a significant role in our everyday lives..

Welcome to Crash Course Statistics! In this series, we're going to take a look at the important role statistics plays in our everyday lives, because statistics are everywhere! Statistics help us better understand the world and make decisions from what you'll wear tomorrow to government policy.

embedded

What Is Statistics: Crash Course Statistics #1

Today we?re going to talk about numeracy - that is understanding numbers. From really really big numbers to really small numbers, it's difficult to comprehend information at this scale, but these are often the types of numbers we see most in statistics.

Mathematical Thinking: Crash Course Statistics #2

Today we?re going to talk about measures of central tendency - those are the numbers that tend to hang out in the middle of our data: the mean, the median, and mode.

Mean, Median, and Mode: Measure of Central Tendency: Crash Course Statistics #3

Today, we're looking at measures of spread, or dispersion, which we use to understand how well medians and means represent the data, and how reliable our conclusions are. They can help understand test scores, income inequality, spot stock bubbles, and plan gambling junkets.

Measures of Spread: Crash Course Statistics #4

Today we're going to start our two-part unit on data visualization. Up to this point we've discussed raw data - which are just numbers - but usually, it's much more useful to represent this information with charts and graphs.

Charts Are Like Pasta - Data Visualization Part 1: Crash Course Statistics #5

Today we?re going to finish up our unit on data visualization by taking a closer look at how dot plots, box plots, and stem and leaf plots represent data.

Plots, Outliers, and Justin Timberlake: Data Visualization Part 2: Crash Course Statistics #6

When collecting data to make observations about the world it usually just isn't possible to collect ALL THE DATA. So instead of asking every single person about student loan debt for instance we take a sample of the population.

The Shape of Data: Distributions: Crash Course Statistics #7

Today we?re going to talk about data relationships and what we can learn from them. We?ll focus on correlation.

Correlation Doesn't Equal Causation: Crash Course Statistics #8

We may be living IN a simulation (according to Elon Musk and many others), but that doesn't mean we don't need to perform simulations ourselves. Today, we're going to talk about good experimental design and how we can create controlled experiments to minimize bias when collecting data.

Controlled Experiments: Crash Course Statistics #9

Today we're going to talk about good and bad surveys. From user feedback surveys, telephone polls, and those questionnaires at your doctor's office, surveys are everywhere, but with their ease to create and distribute, they're also susceptible to bias and error.

Sampling Methods and Bias with Surveys: Crash Course Statistics #10

We?ve talked a lot in this series about how often you see data and statistics in the news and on social media - which is ALL THE TIME! But how do you know who and what you can trust? Today, we?re going to talk about how we, as consumers, can spot flawed studies, and sensationalized articles.

Science Journalism: Crash Course Statistics #11

Today we?re going to talk about ethical data collection.

Henrietta Lacks, the Tuskegee Experiment, and Ethical Data Collection: Crash Course Statistics #12

Today we?re going to begin our discussion of probability.

Probability Part 1: Rules and Patterns: Crash Course Statistics #13

Today we're going to introduce Bayesian statistics and discuss how this new approach to statistics has revolutionized the field from artificial intelligence and clinical trials to how your computer filters spam!

Probability Part 2: Updating Your Beliefs with Bayes: Crash Course Statistics #14

Today we're going to discuss the Binomial Distribution and a special case of this distribution known as a Bernoulli Distribution. The formulas that define these distributions provide us with shortcuts for calculating the probabilities of all kinds of events that happen in everyday life.

The Binomial Distribution: Crash Course Statistics #15

Geometric probabilities, and probabilities in general, allow us to guess how long we'll have to wait for something to happen. Today, we'll discuss how they can be used to figure out how many Bertie Bott's Every Flavour Beans you could eat before getting the dreaded vomit flavored bean.

Geometric Distributions and The Birthday Paradox: Crash Course Statistics #16

There are a lot of events in life that we just can?t predict, but just because something is random doesn?t mean we don?t know or can?t learn anything about it.

Randomness: Crash Course Statistics #17

Today we?re going to talk about how we compare things that aren?t exactly the same - or aren?t measured in the same way. For example, if you wanted to know if a 1200 on the SAT is better than the 25 on the ACT.

Z-Scores and Percentiles: Crash Course Statistics #18

Today is the day we finally talk about the normal distribution! The normal distribution is incredibly important in statistics because distributions of means are normally distributed even if populations aren't.

The Normal Distribution: Crash Course Statistics #19

Today we?re going to talk about confidence intervals. Confidence intervals allow us to quantify our uncertainty, by allowing us to define a range of values for our predictions and assigning a likelihood that something falls within that range.

Confidence Intervals: Crash Course Statistics #20

Today we're going to begin our three-part unit on p-values.

How P-Values Help Us Test Hypotheses: Crash Course Statistics #21

Today we?re going to discuss some problems with the logic of p-values, how they are commonly misinterpreted, how p-values don?t give us exactly what we want to know, and how that cutoff is arbitrary - and arguably not stringent enough in some scenarios.

P-Value Problems: Crash Course Statistics #22

We're going to finish up our discussion of p-values by taking a closer look at how they can get it wrong, and what we can do to minimize those errors.

Playing with Power: P-Values Pt 3: Crash Course Statistics #23

Today we’re going to talk about Bayes Theorem and Bayesian hypothesis testing. Bayesian methods like these are different from how we've been approaching statistics so far because they allow us to update our beliefs as we gather new information.

You Know I'm All About that Bayes: Crash Course Statistics #24

Today we?re going to talk about Bayes Theorem and Bayesian hypothesis testing. Bayesian methods like these are different from how we've been approaching statistics so far, because they allow us to update our beliefs as we gather new information.

Bayes in Science and Everyday Life: Crash Course Statistics #25

Test statistics allow us to quantify how close things are to our expectations or theories. Instead of going on our gut feelings, they allow us to add a little mathematical rigor when asking the question: ?Is this random? or real?"

Test Statistics: Crash Course Statistics #26

Today we're going to walk through a couple of statistical approaches to answer the question: "is coffee from the local cafe, Caf-fiend, better than that other cafe, The Blend Den?"

T-Tests: A Matched Pair Made in Heaven: Crash Course Statistics #27

Today we're going to talk about degrees of freedom - which are the number of independent pieces of information that make up our models. More degrees of freedom typically mean more concrete results.

Degrees of Freedom and Effect Sizes: Crash Course Statistics #28

Today we're going to talk about Chi-Square Tests - which allow us to measure differences in strictly categorical data like hair color, dog breed, or academic degree.

Chi-Square Tests: Crash Course Statistics #29

Today, we're going to talk about a few ways researchers have "hacked" their data, and give you some tips for identifying and avoiding these types of problems when you encounter stats in your own lives.

P-Hacking: Crash Course Statistics #30

Replication (re-running studies to confirm results) and reproducibility (the ability to repeat an analyses on data) have come under fire over the past few years.

The Replication Crisis: Crash Course Statistics #31

Today we're going to build a hypothetical model to look at the relationship between likes and comments on a trending YouTube video using the Regression Model.

Regression: Crash Course Statistics #32

Today we're going to continue our discussion of statistical models by showing how we can find if there are differences between multiple groups using a collection of models called ANOVA.

ANOVA: Crash Course Statistics #33

Last week we introduced the ANOVA model which allows us to compare measurements of more than two groups, and today we?re going to show you how it can be applied to look at data that belong to multiple groups that overlap and interact.

ANOVA Part 2: Dealing with Intersectional Groups: Crash Course Statistics #34

Today we're going to wrap up our discussion of General Linear Models (or GLMs) by taking a closer looking at two final common models: ANCOVA (Analysis of Covariance) and RMA (Repeated Measures ANOVA).

Fitting Models Is like Tetris: Crash Course Statistics #35

We've talked a lot about modeling data and making inferences about it, but today we're going to look towards the future at how machine learning is being used to build models to predict future outcomes.

Supervised Machine Learning: Crash Course Statistics #36

Today we're going to discuss how machine learning can be used to group and label information even if those labels don't exist.

Unsupervised Machine Learning: Crash Course Statistics #37

Today we're going to focus on some ways Big Data impacts on our lives from what liking Hello Kitty says about us to how Netflix chooses just the right thumbnail to encourage us to watch more content.

Intro to Big Data: Crash Course Statistics #38

There is a lot of excitement around the field of Big Data, but today we want to take a moment to look at some of the problems it creates.

Big Data Problems: Crash Course Statistics #39

As we near the end of the series, we're going look at how statistics impacts our lives. Today, we're going to discuss how statistics is often used and misused in the courtroom.

Statistics in the Courts: Crash Course Statistics #40

Today we're going to talk big picture about what Neural Networks are and how they work. Neural Networks, which are computer models that act like neurons in the human brain, are really popular right now - they're being used in everything.

Neural Networks: Crash Course Statistics #41

Today we're going to discuss the role of statistics during war. From helping the Allies break Nazi Enigma codes and estimate tank production rates to finding sunken submarines, statistics have and continue to play a critical role on the battlefield.

War: Crash Course Statistics #42

Today we're going to talk about why many predictions fail - specifically we'll take a look at the 2008 financial crisis, the 2016 U.S. presidential election, and earthquake prediction in general.

When Predictions Fail: Crash Course Statistics #43

In our series finale, we're going to take a look at some of the times we've used statistics to gaze into our crystal ball, and actually got it right!