All stat teachers are 😢😢😢

It is easy to get distracted in a long lesson, shorter compact videos are easier to understand Dante general picture of a subject

yeah..

exam in 9 hours... wish me luck

@@jackwaterman8185 lo.l

@@jackwaterman8185 Did the luck work?

watching a khan academy video DURING an online exam is a whole new level of pain Covid 19 has invented

lol

bahaha! I kept thinking the same thing while watching it! lol!!

That straight line at 2:12 tho

@@andreathompson7136 Omg me too!

@@KPheeze he didn't draw that it's a tool

right? same here! what a coincidence!

@Finn You do realize each segment has like 5-6 videos that explains the general concept then goes into more specific problems.. lol

@Finn What other source?

@Finn DON'T LEAVE US HANGING LIKE THAT BROTHER, GIVE LINKS TO THE PEOPLE xD xD

One year later and @Finn still hasn't given us the alternate source. The internet is still such a cruel place...

i thought i was the only one, he lost me... didnt know how he got 0.44 and 0.64, made this video pointless to me

Jad Sayegh yeah, it's an interesting mistake Sal made here.

Yeah, this is a clear mistake. The confidence interval must be treated as itself a random variable. 95% confidence level means only that 95 confidence intervals out of 100 generated from repeated experiments will contain the population parameter. Once a confidence interval of a specific experiment is generated, the population parameter either exists within it or it doesn't. Probabilistic thinking at that point is no longer valid.

the same came to my mind, while watching it. Isn't this statement: "There's is a 95% probability that the population parameter is within the interval" the interpretation of bayesian inference (which we do not have here)? In terms of frequentists we would say: "the ensemble of all confidence intervals (calculated based on samples) would capture population parameter in 95% # of cases" which is not the same. We in fact do not know this "interval" that would cover population parameter with 95% probability.

Hi, I know I'm late but I have a statistics exam in a week and I don't fully understand why the statement is wrong? Could you maybe explain it?

@@evertbronstring5219 how did you do

Good luck!

i passed

@@joshbux99 NICE! I may take AP Statistics next year for 2021-2022 as a Senior in High School. Any tips?

@@danielinostroza7122 every course is different but one piece of advice that works across the board is pay attention and ask questions. i fell behind for part of the semester because i didn’t pay attention for two classes and spent the rest of the time playing catch up

@@joshbux99 Would you say the course requires self-motivation and self-accountability? Interestingly, this semester went really well for me because I held myself accountable to do my work, which was something in-person school didn't require me to do. I've found that online school has had me make myself accountable for the schoolwork I need to do more than any previous school year

Jen _29 you’re not alone😂

asdFRap yeah it’s annoying that they have video mixed up. You can find this on the app but can’t play it there. You have to go to the browser app and then open it in youtube.

Tag me on it when you do.

912sonic Exactly

No. SE works for any interval, your margin of error is what changes depending on you CI.

bear beets, battlegalatica. 👌

@@potatonoodlebear8035 False.

Go watch the earlier videos

Because he was using a confidence level (CL) of 95% and the population is assumed to be normally distributed. Though confidence levels & confidence intervals are two different thing, I find it easiest to think of them together by reminding myself of what concluding statement I'm making using my sample finding. In the video's, I, the sampler, am saying : "I randomly selected (aka, sampled) 100 people from a population of 100,000 and I found that 54 of them (0.54 of the whole sample or 54% of the people sampled) prefer Candidate A. Based on that sample finding, I am 95% sure that were I to have asked the same question of every member of the population, I would have found that 54%, plus or minus 0.10, of the population preferred the same candidate as did the 100 people whom I surveyed." So, with that in mind, let's look at the math. Step 1: I got 0.54 in favor of Candidate A, so I will put 0.54 in the center of my normal distribution curve and call it the "sample mean" or "sample proportion." Step 2: Ask myself, "How many std deviations away from my sample mean - i.e., how wrong can I be - can the actual population mean be and still have my above stated conclusion still be true?" The answer to that, for a 95% confidence level, is 2 std deviations. For a 99% confidence level, it would be 3 std deviations. Step 3: Ask myself , "How far numerically is 2 std deviations from my sample mean?" To determine that, I need to know how far one std deviation is from my sample mean, and then I will multiply it by 2. Step 4: Calculate the std error. The std error that Sal calculated is the distance you must go (left or right) to get to one std deviation from your sample mean. In the video's sample, Sal calculated the std error as 0.05. Tip -> Do yourself a favor and calculate the std error assuming Sal's sample size was 55, 85, 150, & 350. That will allow you to see how the std error will increase or decrease. Step 5: Multiply the std error by 2. Why by 2? Because 2 std deviations (aka, std error's worth of distance) is how far the 95% confidence level is from the sample mean for a normally distributed population. (Can one have a not normally distributed population? Yes, but that's a different lesson & a whole new "ballgame.") Since the std error was 0.05, twice that is 0.10. Step 6: Determine Confidence Interval The confidence interval is the distance from (point A) 2 std deviations below (left of) of the sample mean to (point B) 2 std deviations above (right of) the sample mean. -> Since std error = 1 std deviation, twice the std error subtracted from the sample mean gives us "point A," which is the lower limit of the confidence interval. -> Doubling the std error and adding it to the sample mean gives US the upper limit of the confidence interval. What's 0.54 - 0.10? 0.44. What's 0.54 + 0.10? 0.64. And, voila. The confidence interval is 0.44 to 0.64. Now that you see how we calculated the confidence interval, do you know what it means, what it tells you? The confidence interval tells you how wrong the value you got as the sample mean - i.e., the percentage of people in the sample who preferred Candidate A - could possibly be. In other words, for the sample in the video, the confidence interval says: "Because I know that my sample merely estimates, rather than actually counts or measures, what share of the population actually prefers Candidate A, I know that my estimate could be off by a bit. How off? Well, by as much as 0.10, or 10 percentage points in either direction. That is to say, it's possible that anywhere from 44% to 64% of the population prefers Candidate A." Realizing that's what the confidence interval is telling you, you should also be aware of one very important thing: samples indicate what was so at the time the poll was taken. They do not indicate what will be so in the future. Thus context is critical to interpreting a poll's findings and the germanity of them given the confidence interval. For example, if Sal's poll were of the percentage of quartz found in bituminous coal deposits, the 20 point wide confidence interval may not be too big of a deal because the composition of coal deposits doesn't change much. But Sal sampled people's opinion, and people's minds change a lot. So you have to ask yourself: "Given the confidence interval is 20 points wide, does it make sense to rely on the poll's finding as a predictor of who will win the election that will occur at some time in the future? I hope the preceding discussion, long though it is, helps you understand how the confidence interval was determined and, more importantly, what a confidence interval tells you and the types of things you should think about upon seeing it and the thing that was sampled.

@@anthonybell6156 God bless you Sir! You should make school textbooks. I did my homework just by following your 6 steps and your notes at the end. I cannot thank you enough! (_)(_):::::::::::::::::::::::::::D~~~~~~~~~~

.46 = (1 - P' ) = (1 - 0.54) where P' = 0.54 is the sample proportion

@@61percentodicarica thanks.

cause that's how it is. 2 standard deviations will include 95% of the data, thats just a stats "rule" its not a variable in the problem