Central limit theorem | Inferential statistics | Probability and Statistics | Khan Academy

Introduction to the central limit theorem and the sampling distribution of the mean Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/sampling_distribution/v/sampling-distribution-of-the-sample-mean?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistics-inferential/normal_distribution/v/ck12-org-more-empirical-rule-and-z-score-practice?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy

Comments

1. centreal limit theorem for dummies huh? I sure am a dummy so no problem with that for sure.
2. This is a boring, rambling, over-explanation that never seemed to get to the point until the last minute. I was better off google'ing the definition than listening to his longwinded anecdotes. GET. TO. THE. POINT. I'm not here for a comedian or to listen to a verbal flood. The reason people are here is because they're having a hard time learning with their peers. Meaning they need the bottom line, faster, with less distractions from concepts, anecdotes, and words that aren't directly and immediately related to the topic.
3. Hi do you do tutoring offline?
4. Great video!
5. Good to know there are 800 thousand people who also didn't understand this
6. How would you calculate the mean and standard of the sample distribution that would result from take n number of samples from a distribution?
7. You, sir, are The Real MVP!
8. I swear to god he keeps saying meme and not mean. The internet is ruining me XD
9. Does that mean normally distributed data is a characteristic of not the population distribution but the act of drawing a simple random sample itself?
10. The man is so not right about the concept. For the central limit theory to apply n >30. He constructed a normally distributed curve using a sample size of 4, i.e n=4. Are the other viewers delusional ?? I think the guy might have been high on acid.
11. Man there's an error. You cant have more than 256 samples. Do the math.
12. Cool
13. Drinking game: drink whenever you hear the word "sample"
14. in one word, gazilion!
15. Heck yeah, this is a great motivating video... gives an outline of the idea and why it's so cool and important!
16. I mean this is brilliant! Got me thinking and understanding deeply.
17. If x = 1,3,4 or 6 and the sample size is 4, there would be 4*4*4*4 possibilities i.e. 4^4 possibilities =a maximum of 256 possible outcomes so by taking 10,000 samples you will be repeating each 1 about 40 times.
18. how do you denote the number of times you have samples? example: so you say sample size n=4, but 100 of those samples, how do we denote that?
19. He says that with an infinite sample size, it approaches a normal distribution, but shouldn't it just be a straight line at the mean? (albeit this is a normal distribution with a standard deviation of 0)
20. Simple is beautiful