Crash Course Statistics - Season 1 Episode 11 Science Journalism
Season - Episode
1
Season 1 Jan 23, 2018
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1 - 1What Is Statistics Jan 24, 2018 -
1 - 2Mathematical Thinking Jan 31, 2018
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1 - 3Mean, Median, and Mode: Measures of Central Tendency Feb 07, 2018
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1 - 4Measures of Spread Feb 14, 2018
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1 - 5Charts Are Like Pasta - Data Visualization Part 1 Feb 21, 2018
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1 - 6
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1 - 7The Shape of Data: Distributions Mar 07, 2018
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1 - 8Correlation Doesn’t Equal Causation Mar 14, 2018
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1 - 9Controlled Experiments Mar 21, 2018
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1 - 10Sampling Methods and Bias with Surveys Mar 28, 2018
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1 - 11Science Journalism Apr 11, 2018
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1 - 12
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1 - 13Probability Part 1: Rules and Patterns Apr 25, 2018
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1 - 14Probability Part 2: Updating Your Beliefs with Bayes May 02, 2018
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1 - 15The Binomial Distribution May 09, 2018
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1 - 16Geometric Distributions and The Birthday Paradox May 16, 2018
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1 - 17Randomness May 23, 2018
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1 - 18Z-Scores and Percentiles May 30, 2018
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1 - 19The Normal Distribution Jun 06, 2018
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1 - 20Confidence Intervals Jun 13, 2018
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1 - 21How P-Values Help Us Test Hypotheses Jun 27, 2018
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1 - 22P-Value Problems Jul 11, 2018
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1 - 23Playing with Power: P-Values Pt 3 Jul 18, 2018
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1 - 24You Know I’m All About that Bayes Jul 25, 2018
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1 - 25Bayes in Science and Everyday Life Aug 01, 2018
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1 - 26Test Statistics Aug 08, 2018
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1 - 27T-Tests: A Matched Pair Made in Heaven Aug 15, 2018
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1 - 28Degrees of Freedom and Effect Sizes Aug 22, 2018
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1 - 29Chi-Square Tests Aug 29, 2018
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1 - 30P-Hacking Sep 05, 2018
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1 - 31The Replication Crisis Sep 26, 2018
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1 - 32Regression Oct 03, 2018
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1 - 33ANOVA Oct 10, 2018
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1 - 34ANOVA Part 2: Dealing with Intersectional Groups Oct 17, 2018
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1 - 35Fitting Models Is like Tetris Oct 24, 2018
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1 - 36Supervised Machine Learning Oct 31, 2018
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1 - 37Unsupervised Machine Learning Nov 07, 2018
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1 - 38Intro to Big Data Nov 14, 2018
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1 - 39Big Data Problems Nov 21, 2018
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1 - 40Statistics in the Courts Nov 28, 2018
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1 - 41Neural Networks Dec 12, 2018
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1 - 42War Dec 19, 2018
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1 - 43When Predictions Fail Jan 02, 2019
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1 - 44When Predictions Succeed Jan 09, 2019
Overview
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, sensationalized articles, and just plain poor reporting. And this isn’t to say that all science articles you read on facebook or in magazines are wrong, but that it's valuable to read those catchy headlines with some skepticism.
Year 2019
Studio YouTube
Director
Crew
Popularity 0.2338
Language English
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