Delivered casually, while bringing out subtle points very sharply. By far the most lucid explanation I've seen. Thanks for taking the time to make the video and for giving it to the world for free!
Ngl, that first question was hard for me. I had to attentively watch the solution to get a solid understanding of the concept. But then the second question became a breeze for me once I familiarized myself with the underlying statistical ideas. Feel much more confident about my knowledge of Hypothesis Testing now. Thanks for making such high-quality content! Really appreciate it :)
I hazard a guess that were this video broken into two smaller chunks there would be more views. Some people are intimidated by longer content or have short attention spans. It’s a shame because this content is top class. 👏🏻
Amazing videos!!! You have made all the statistics concepts easy to digest and understand! Thanks a lot and please keep it up!!! P.S: just found out that your videos are being used as our lecture recording... WOWWW...
I am bad at statistical methods. you follow an intuitive approach that helps. but i need more examples to understand what those formulae in most books mean and when to use which one. hope you keep making such videos.
Only halfway through this video but this video is really helpful for getting an intuitive understanding of the concepts for hypothesis testing. Thank you!!
great video and illustration. I really like the big map and putting all the details in one long video, very comprehensive and saved my time of finding all short scattered video.
17:27 For both cases, to evaluate the variance of p Var(p)=Var(N)/N_t^2, one needs the variance of N, Var(N), the latter can be evaluated using E(N)=p(d/dp)(p+q)^Nt and E(N(N-1))=d^2(d^2/dp^2)(p+q)^Nt, where q=1-p, and p=p_0 or p_1 and Nt is the number of total samples, such as n_0 and n_1. I kinda think the derivation is omitted in the video (is there a more straightforward way to see it?) so write it down here a side note.
For the power calculation, why is the T1 statistic normalized to the standard error of the null hypothesis, sqrt(V_H0), and not the standard error of the alternative hypothesis sqrt(V_H1), because later on you use 0.1 as the theta_hat and not 0.
For Part (a), I did something slightly different. I calculated the point on the x axis where the H0 curve at the 95% mark. I got 0.058154 (I know spurious accuracy). I then calculated how much of the H1 curve was to the left of 0.58154 (mean 0.1, sd 0.035) and subtracted it from 1. I did it this way so I would understand where 2.8284 had come from.
i did my problem similar to your process but my 95% mark is coming as 0.11567685 could you help me in how you got your value or what i may be doing wrong( i used excel function of norm.dist with mean of 0 stdev = 0.70711 and then goal seeked my x value) thanks!
Thank you very much for this comprehensive and intuitive video on hypothesis testing. I was wondering if we could get this example in code. Maybe in python or another technology or maybe suggest us another video that works on this. Thank you again I feel that this video helped me more than anything in understandying deeply those concepts.
You will make a really good cricket commentator. You got that voice 😀 But pls don’t quit making tutorials. Thank you for very clearly explained videos.
At 39:29, you say confidence interval crosses zero because p=0.58 is greater than 0.05. Could you clarify how to infer it crosses zero if calculated p value is greater than 0.05 ?
As always, amazing it is. On the first example, while standardizing the normal distributions, the test statistic which was used was "T". Why isn't it Z statistic? (I'm just a beginner here, sorry for the question)
Sample size was large enough for a z-statistic to be used, instead of the t-statistic. T-statistic is for very small samples/observations. Z-statistic is for large samples/observations. (*Usually, more than 30 observations - use the Z-statistic; less than that - T-statistic. )
Any chance you still respond to questions> Preparing for an exam and i am unsure at 1:03:22 when testing power, how you got the value of 0.1159. Thank you for your help bud
Thank you so much, I've been watching the videos on your channel and they've really helped me to develop my intuition into the difference procedures. Although, I still get stuck on the 2-tail test being more stringent than the 1-tail test - so it is harder to show that the mean is not what we think that it is than it is to show that the mean is larger than we think it is... ??? It will take a while to get used to.
@13.58... I'm doing a retrospective on our experimental design choices....... we got a result on one side.... why did we get a t-statistic on the right-side? because we set out parameter estimate as p1-p0... if we set our parameter estimate as p0-p1 we would have got the t-statistic on the other side of the tail-end.... More importantly, It occurred to me that p1 and p0 are defined as positive outcomes (asking is there a sig difference in one therapy having more positive-outcome than the other?), but if we did negative outcomes instead (asking is there a sig difference in one therapy having more negative-outcome than the other?), I suspect we would still be able to reject the null hypothesis, but we would be working with a different normal distribution and then depending on how we setup our parameter estimate we would get a t-statistic on one end or the other.... BUT both questions should lead to the same conclusion.... self-consistent with each other... I don't know if its worth doing twice the work... but it might give confidence that the therapies have normal distribution.... which would reinforce the self-consistency, thus the validity of the test.
This is a brilliant video! I love the Zedstatistics series. Query: I learned the 0.05 level of (in)significance was a product of the 95% confidence interval (the other 95% under the curve includes 2 standard errors). Is this wrong?
p0 is the probability of the positive outcome of the operated group, it is actually a **Bernoulli** distribution with the outcome being YES (with probability p0) or NO (1-p0). The variance of Bernoulli distributions is p*(1-p), and because it is a **sampled** distribution, the variance needs to be divided by n.
Very well explained in the video. The method of hypothesis testing curve would work well in case of binary events, as the variances of null and alternate hypothesis curves have been calculated using the binomial distribution formulas. How to draw the hyposethis curves when the event outcome is more than binary, say three or more possibile outcome?
Can someone explain why the standard error is just the root of the variance? I thought it was the standard deviation divided by the squareroot of theobservations. Or is this somehow the same?
I wondered that as well at first. But I think the reason is that here we care about the standard error of an estimator for which we already calculated the variance, which includes the number of observations. The formula you are referring to is the standard error for a mean estimator where you only know the variance (or standard deviation for that matter) of a sample, not the estimator. I hope what I'm saying is clear and I also hope the reasoning I came up with is correct...
Hi great video, At 4.55 mins, a graph pops out. Please correct if I am wrong, no way you will be able to see a plot like what you show if you were to toss A coin 100 times . are you implying tossing 1 coin 100 times and repeating this experiment N no of times ?
This distribution is not the distribution of random experiments or distribution we draw after tossing 100 times. In other words, It is not the sample distribution after making maximum liklihood estimation. It is just a binomial distribution of a fair coin. if you made the permutations you will find that equal number of heads and tails is the most frequent pattern. This curve shows the probability of getting for example 1 tails in 100 trials and 2 tails in 100 trials ...... and 100 tails in 100 trials. by trials i mean only one toss. This curve can also be generated by experiments as trials tend to infinity or by making many samples each sample contains a number of trials and then get the average of all the samples it will tend to 50.
At the 28:23 mark, I am confused by the conclusion :'...operative patients did better than the physio only patients'. This is a two tailed sample test. H1: p1 p2. So, if H0 is rejected, it can only approved that p1 p2. We can not refer that p1> p2. Please clarify. Thanks!
I think there is an error at around 26:00. You are inserting p-hat (i.e. the proportions measured in your sample) for the "true" proportions p given by the 0-hypotheses. Shouldn't the resulting t be t-distributed instead of normal-distributed?
During the prediction of sampling statistic distribution, why the number of observation for p1 and p0 is different (i.e. n1 and n0) since if we are finding θ, the number of observations for the proportion of positive outcomes for both non-operative and operative should be same.....?
Thank you! can I ask you which software you are using to show your slides. I know that zooming can be done using Ms. Powerpoint, however not all possible.
Where exactely does that formula for the variance come from? In your other video on variance and standard deviation it is a totally different formula :(
If you're talking about the surgery example in the beginning then it comes from binomial distribution. Learn about central limit theorem and binomial distribution you will easily understand it.
Well, ideally the value is whatever you want it to be. It just happens to be good practice to choose 0.005. Nothing says it can’t be different. I think a p value of 0.005 works well in most cases so it just became accepted as a standard.
At video 58 minutes why do you not divide by n-1 or 400-1=399 instead of 400. This is an important concept I do not understand. One never knows the true variance and only knows the same variance. Therefore I would expect the denominator to be 399 to reflect n-1. Respectfully submitted--WhetstoneGuy
Awesome video :) Couldn't wrap my head around why is the variation of the red and black distribution different in the second exercise? Please advise if possible, thanks
Thanks for the great lecture. I'm new to statistics, I have a question regarding the test statistic used in this video. is the formula used in this video generalized test statistic or any specific test statistic ? I have read about Z-test , T-test given mean and standard deviation, sample size of population and sample. is power calculation applicable for only when proportion values are given ? It's little confusing for me.
at 25:54 why you chose to use pooled proportion BUT at 35:25 you did not use pooled proportion? I used θ ÷ sqrt(p1q1/n1 + p0q0/n0) as my test statistic which leads me to t=2.009868 is that okay as well?
@@harryfeng4199 i forgot how to do statistics nowadays 😂 but i think i got it when reviewing it today because of your reply. Note that at 25:54 we assume Null Hyp: p1-p2=0 but when calculating confidence interval, we have p1-p2≠0 instead. e.i. p1-p2=0.14 in that case, we dont use pooled proportions since at 35:54 we dont assume p1=p2 anymore unlike in Null Hyp at 25:54
Hello sir. Why does theta have to equal "p1-p0=0" ? If they both subtract to give 0, then why can't one say: "p1=po"? Are different formulas used between these two ways to describe the null hypothesis?
Hi, firstly of all thanks from the bottom of my heart for this video. Secondly, why we can't have sameness in our alternative hypothesis? The distribution of difference at 16:18 would just have a higher number as a mean and the decreasing differences on the both sides. Where beyond a critical value the sameness should exist?
Princess Diana is upset today, because you didn't remind people about Welch's t-test?! I think it's fair to remind people at an introductory level, that there are different tests that use the same distribution.... just because the 'Student t-test' has a lot t's in it, it is not the only test that accompanies the t-distribution... I discovered today...
Jargon Followup: with regard to Welch test vs Student test what are the associated distributions; are they the same or different? is just the test different, but they use the same distribution for scoring? Lets step back: what is the difference between 'test', and the "score"... the test is the equation that produces a score, and the "score/value/statistic" is the point on an x-axis on a histogramic distribution. In most/all cases the distribution is related to the test via its letter... ex t-test is to t-distribution what z-test is to z-distribution.... this means both the welch t-test and the student t-test use the same t-distribution to determine a t-score/t-value/t-statistic...
Actually, I think I got information overload... we are actually not using a t-distribution, we are using a normal distribution... but I am confusing the terminology of 't-statistic' with the terminology of 'test-statistic', the later being a more general term for the results of any test regardless of distribution... (ie a t-statistic is a test-statistic associated with a t-test and a t-distribution).
Oh wow! another interesting jargon-fact: the Standard Normal Distribution N(0,1) is also called the z-distribution, so we are doing a z-test, I presume... but you tried to shield us from all the horrible jargon! I better understand and appreciate your pedagogy in this cruel world! Mr Zedstatistics in deed!
Hi, I am just wondering if anyone knows why we used a T- distribution for the hypothesis test but a Z distribution for the confidence interval at 37:36?
Delivered casually, while bringing out subtle points very sharply. By far the most lucid explanation I've seen. Thanks for taking the time to make the video and for giving it to the world for free!
do you understand his "proof" of why they variance of the T statistics equals to 1 @ 22:58? Would you mind explaining it to me?
⁰
ruclips.net/video/RkL3cG5QHbE/видео.html
Ngl, that first question was hard for me. I had to attentively watch the solution to get a solid understanding of the concept. But then the second question became a breeze for me once I familiarized myself with the underlying statistical ideas. Feel much more confident about my knowledge of Hypothesis Testing now.
Thanks for making such high-quality content! Really appreciate it :)
The most under-rated(fewer views for an extraordinary content)
video on youtube
Thanks ! Well I don't advertise the channel but feel free to tell all your statistically minded friends :)
I hazard a guess that were this video broken into two smaller chunks there would be more views. Some people are intimidated by longer content or have short attention spans. It’s a shame because this content is top class. 👏🏻
00000000000000000000000000000⁰⁰0
One of the best channels ❣️ i enjoy learning from your videos. Thank you so much 🙏😇
No ads. Thanks for doing this👍
Amazing videos!!! You have made all the statistics concepts easy to digest and understand! Thanks a lot and please keep it up!!!
P.S: just found out that your videos are being used as our lecture recording... WOWWW...
I am bad at statistical methods. you follow an intuitive approach that helps. but i need more examples to understand what those formulae in most books mean and when to use which one. hope you keep making such videos.
ruclips.net/video/RkL3cG5QHbE/видео.html
Thank you, your videos have helped change my life!
Only halfway through this video but this video is really helpful for getting an intuitive understanding of the concepts for hypothesis testing. Thank you!!
ruclips.net/video/RkL3cG5QHbE/видео.html
If my stat teacher can teach 10% as clearly as in this video...
I can feel your pain😥
ruclips.net/video/RkL3cG5QHbE/видео.html
great video and illustration. I really like the big map and putting all the details in one long video, very comprehensive and saved my time of finding all short scattered video.
ruclips.net/video/RkL3cG5QHbE/видео.html
17:27 For both cases, to evaluate the variance of p Var(p)=Var(N)/N_t^2, one needs the variance of N, Var(N), the latter can be evaluated using E(N)=p(d/dp)(p+q)^Nt and E(N(N-1))=d^2(d^2/dp^2)(p+q)^Nt, where q=1-p, and p=p_0 or p_1 and Nt is the number of total samples, such as n_0 and n_1. I kinda think the derivation is omitted in the video (is there a more straightforward way to see it?) so write it down here a side note.
the examples really opened my eyes on statistics, very well done!
For the power calculation, why is the T1 statistic normalized to the standard error of the null hypothesis, sqrt(V_H0), and not the standard error of the alternative hypothesis sqrt(V_H1), because later on you use 0.1 as the theta_hat and not 0.
For Part (a), I did something slightly different.
I calculated the point on the x axis where the H0 curve at the 95% mark. I got 0.058154 (I know spurious accuracy). I then calculated how much of the H1 curve was to the left of 0.58154 (mean 0.1, sd 0.035) and subtracted it from 1. I did it this way so I would understand where 2.8284 had come from.
ruclips.net/video/RkL3cG5QHbE/видео.html
i did my problem similar to your process but my 95% mark is coming as 0.11567685 could you help me in how you got your value or what i may be doing wrong( i used excel function of norm.dist with mean of 0 stdev = 0.70711 and then goal seeked my x value) thanks!
Thank you very much for this comprehensive and intuitive video on hypothesis testing. I was wondering if we could get this example in code. Maybe in python or another technology or maybe suggest us another video that works on this. Thank you again I feel that this video helped me more than anything in understandying deeply those concepts.
Excellent video as usual. One edit, if I may, at 31:19, it should be p
@ 22:58 why on earth the variance divided by the variance squared should be equal to 1??
ruclips.net/video/RkL3cG5QHbE/видео.html
You will make a really good cricket commentator. You got that voice 😀 But pls don’t quit making tutorials. Thank you for very clearly explained videos.
I am still confused about the variance linear algebra . is there anyone can help to explain a bit?
@ 22:58 why on earth the variance divided by the variance squared should be equal to 1??
At 39:29, you say confidence interval crosses zero because p=0.58 is greater than 0.05. Could you clarify how to infer it crosses zero if calculated p value is greater than 0.05 ?
As always, amazing it is.
On the first example, while standardizing the normal distributions, the test statistic which was used was "T". Why isn't it Z statistic? (I'm just a beginner here, sorry for the question)
Sample size was large enough for a z-statistic to be used, instead of the t-statistic.
T-statistic is for very small samples/observations.
Z-statistic is for large samples/observations.
(*Usually, more than 30 observations - use the Z-statistic; less than that - T-statistic. )
Less than 30 sample we use T statistics and for samples above 30 we use Z score!!!
Your way of teaching is AMAZING
Any chance you still respond to questions> Preparing for an exam and i am unsure at 1:03:22 when testing power, how you got the value of 0.1159. Thank you for your help bud
You are the best!!
Thank you for this video!
Thank you so much, I've been watching the videos on your channel and they've really helped me to develop my intuition into the difference procedures.
Although, I still get stuck on the 2-tail test being more stringent than the 1-tail test - so it is harder to show that the mean is not what we think that it is than it is to show that the mean is larger than we think it is... ??? It will take a while to get used to.
ruclips.net/video/RkL3cG5QHbE/видео.html
Great teaching! But at 17:05 variance and Linear Algebra are associated. What is the connection?
ruclips.net/video/RkL3cG5QHbE/видео.html
@13.58... I'm doing a retrospective on our experimental design choices....... we got a result on one side.... why did we get a t-statistic on the right-side? because we set out parameter estimate as p1-p0... if we set our parameter estimate as p0-p1 we would have got the t-statistic on the other side of the tail-end.... More importantly, It occurred to me that p1 and p0 are defined as positive outcomes (asking is there a sig difference in one therapy having more positive-outcome than the other?), but if we did negative outcomes instead (asking is there a sig difference in one therapy having more negative-outcome than the other?), I suspect we would still be able to reject the null hypothesis, but we would be working with a different normal distribution and then depending on how we setup our parameter estimate we would get a t-statistic on one end or the other.... BUT both questions should lead to the same conclusion.... self-consistent with each other... I don't know if its worth doing twice the work... but it might give confidence that the therapies have normal distribution.... which would reinforce the self-consistency, thus the validity of the test.
Thank you so much for this teaching
Clear and informative
ruclips.net/video/RkL3cG5QHbE/видео.html
Thank you sir!!
- raph
"We are attracted to it because it's nice and round" lol I don't feel that the choice of words here was totally innocent.
This is a brilliant video! I love the Zedstatistics series. Query: I learned the 0.05 level of (in)significance was a product of the 95% confidence interval (the other 95% under the curve includes 2 standard errors). Is this wrong?
ruclips.net/video/RkL3cG5QHbE/видео.html
While calculating expected value of T1, why variance of H0 is used instead of variance of H1?
I love you! Greetings from Sweden
@18:10 Why the variance of the theta is p*(1-p)/(1/n1+1/n0)? variance for binomial distribution is p*(1-p)*n right????
I'd guess that binomial distribution is a distribution of sums of outcomes. And here we are talking about proportions.
p0 is the probability of the positive outcome of the operated group, it is actually a **Bernoulli** distribution with the outcome being YES (with probability p0) or NO (1-p0). The variance of Bernoulli distributions is p*(1-p), and because it is a **sampled** distribution, the variance needs to be divided by n.
A very BIG THANK YOU from Bangladesh
Very well explained in the video. The method of hypothesis testing curve would work well in case of binary events, as the variances of null and alternate hypothesis curves have been calculated using the binomial distribution formulas. How to draw the hyposethis curves when the event outcome is more than binary, say three or more possibile outcome?
ruclips.net/video/RkL3cG5QHbE/видео.html
Life saver!
Great video but I was expecting a t-test in the first example. Why is it a normal distribution?
clt
This is a brilliant video, thanks👍👍
Can someone explain why the standard error is just the root of the variance? I thought it was the standard deviation divided by the squareroot of theobservations. Or is this somehow the same?
I wondered that as well at first. But I think the reason is that here we care about the standard error of an estimator for which we already calculated the variance, which includes the number of observations. The formula you are referring to is the standard error for a mean estimator where you only know the variance (or standard deviation for that matter) of a sample, not the estimator. I hope what I'm saying is clear and I also hope the reasoning I came up with is correct...
Hi great video,
At 4.55 mins, a graph pops out. Please correct if I am wrong, no way you will be able to see a plot like what you show if you were to toss A coin 100 times . are you implying tossing 1 coin 100 times and repeating this experiment N no of times ?
This distribution is not the distribution of random experiments or distribution we draw after tossing 100 times. In other words, It is not the sample distribution after making maximum liklihood estimation. It is just a binomial distribution of a fair coin. if you made the permutations you will find that equal number of heads and tails is the most frequent pattern.
This curve shows the probability of getting for example 1 tails in 100 trials and 2 tails in 100 trials ...... and 100 tails in 100 trials. by trials i mean only one toss.
This curve can also be generated by experiments as trials tend to infinity or by making many samples each sample contains a number of trials and then get the average of all the samples it will tend to 50.
I understood what you were saying until the test statistic formula.
Was it coincidence that the critical value was 1.96 and rejection was at 1.99 a difference of 0.03 and alpha 0.05 was p value 0.047?
At the 28:23 mark, I am confused by the conclusion :'...operative patients did better than the physio only patients'. This is a two tailed sample test. H1: p1 p2. So, if H0 is rejected, it can only approved that p1 p2. We can not refer that p1> p2. Please clarify. Thanks!
ruclips.net/video/RkL3cG5QHbE/видео.html
you already have the data, we are testing, we test when we have data and that is our case.
I think there is an error at around 26:00.
You are inserting p-hat (i.e. the proportions measured in your sample) for the "true" proportions p given by the 0-hypotheses. Shouldn't the resulting t be t-distributed instead of normal-distributed?
During the prediction of sampling statistic distribution, why the number of observation for p1 and p0 is different (i.e. n1 and n0) since if we are finding θ, the number of observations for the proportion of positive outcomes for both non-operative and operative should be same.....?
good
Extremely helpful! Thank you so much!
ruclips.net/video/RkL3cG5QHbE/видео.html
Thank you brother.
Many thanks for yet another great video! Now it feels hopeful to me that I can manage this course :).
do you understand his "proof" of why they variance of the T statistics equals to 1 @ 22:58? Would you mind explaining it to me?
Why the variance of the theta is p*(1-p)/(1/n1+1/n0)? I checked the variance for binomial distribution is p*(1-p)*n. Thank you
I had same doubt as welll. Did you get it?
I'd guess that binomial distribution is a distribution of sums of outcomes. And here we are talking about proportions.
How does the sample difference go on to +/- infinity, when P0 and P1 are both probabilities ? (around 20:30)
A savior
Thank you! can I ask you which software you are using to show your slides. I know that zooming can be done using Ms. Powerpoint, however not all possible.
Looks like Prezi to me
Where exactely does that formula for the variance come from? In your other video on variance and standard deviation it is a totally different formula :(
If you're talking about the surgery example in the beginning then it comes from binomial distribution. Learn about central limit theorem and binomial distribution you will easily understand it.
@@kushalvora7682 @18:10 Why the variance of the theta is p*(1-p)/(1/n1+1/n0)? variance for binomial distribution is p*(1-p)*n right????
@@ajaxaj8470 Because each patient has Bernoulli distribution => variance for one patient is p(1-p) and you have n patients so you divide it by n :).
at 6:51, isn't the true probability should be close to 0.08? cause the y axis is probability.
Good stuff!! Thank you
Hey there ! Amazing content! Thank you so much. I have a question, how do I calculate the left critical value?
ruclips.net/video/RkL3cG5QHbE/видео.html
21:00 I may say 0.05 is 5% that is the two-sigma limitation, a lot of standards use two-sigma limitation.
Bravo!
Excellent
Thank you!
do you understand his "proof" of why they variance of the T statistics equals to 1 @ 22:58? Would you mind explaining it to me?
Excellent!
At 1:03:21, did he mean to write .1151 for the cdf (-1.20)?
example is really tough for beginners...try choosing a simple example instead of a complex one....
H1 is wrong at the beginning....
Nice Video!!! But from 59:22 here, I am starting to confusing...
same i have no clue from that exact point
how do you make slides?
Thank uuuuuu
I am sure that several persons might have completed PhD after watching your videos (including me) likely to submit within next two months
Did anyone notice, Justin is probably color blind!! @47:26
fucking good video
Why SE is multiplied with z in CI calculation ruclips.net/video/8JIe_cz6qGA/видео.html
⭐️⭐️⭐️⭐️⭐️
So statistic is basically BS because somebody just decide to choose 0.05
Well, ideally the value is whatever you want it to be. It just happens to be good practice to choose 0.005. Nothing says it can’t be different. I think a p value of 0.005 works well in most cases so it just became accepted as a standard.
First!
Can ANY of this stuff ever be explained without algebra?
At 22:57, why is the standard error just sqrt(var(theta)) and not sqrt(var(theta)/n)?
variance calculation shouldn't be V(p1)-V(p0) ?
no variance adds
You're a star. Thank you
At video 58 minutes why do you not divide by n-1 or 400-1=399 instead of 400. This is an important concept I do not understand. One never knows the true variance and only knows the same variance. Therefore I would expect the denominator to be 399 to reflect n-1. Respectfully submitted--WhetstoneGuy
I can clearly see your ability and understanding of how to present these concepts in a digestible way. You are fantastic at your job :)
Awesome video :) Couldn't wrap my head around why is the variation of the red and black distribution different in the second exercise? Please advise if possible, thanks
You actually make me like statistics! I appreciate the explanations with the very understandable examples.
ruclips.net/video/RkL3cG5QHbE/видео.html
I have never been more confused in my life
Thanks for the great lecture. I'm new to statistics, I have a question regarding the test statistic used in this video. is the formula used in this video generalized test statistic or any specific test statistic ? I have read about Z-test , T-test given mean and standard deviation, sample size of population and sample.
is power calculation applicable for only when proportion values are given ? It's little confusing for me.
ruclips.net/video/RkL3cG5QHbE/видео.html
i like to be very sure in my tests so my alpha is 0.0420
It is clear thanks but to defined hypothesis again teacher
great ! i like your energy
trying so hard to understand :(
Oh my god, I am so stupid
at 25:54 why you chose to use pooled proportion BUT
at 35:25 you did not use pooled proportion?
I used
θ ÷ sqrt(p1q1/n1 + p0q0/n0)
as my test statistic
which leads me to t=2.009868
is that okay as well?
did u manage to figure out y, im confused on that as well
@@harryfeng4199 nope. 😅
@@harryfeng4199 i forgot how to do statistics nowadays 😂 but i think i got it when reviewing it today because of your reply.
Note that at 25:54 we assume
Null Hyp: p1-p2=0
but when calculating confidence interval, we have p1-p2≠0 instead.
e.i. p1-p2=0.14
in that case, we dont use pooled proportions since at 35:54 we dont assume p1=p2 anymore unlike in Null Hyp at 25:54
@@carlostolosa6530 thxxx!
Hello sir. Why does theta have to equal "p1-p0=0" ? If they both subtract to give 0, then why can't one say: "p1=po"? Are different formulas used between these two ways to describe the null hypothesis?
Hi, firstly of all thanks from the bottom of my heart for this video. Secondly, why we can't have sameness in our alternative hypothesis? The distribution of difference at 16:18 would just have a higher number as a mean and the decreasing differences on the both sides. Where beyond a critical value the sameness should exist?
in Example 1 we have binomial distribution which the variance should be np(1-p).
Are you using Prezi making these videos? Or May I know what tool u used to make your videos? TIA
Princess Diana is upset today, because you didn't remind people about Welch's t-test?! I think it's fair to remind people at an introductory level, that there are different tests that use the same distribution.... just because the 'Student t-test' has a lot t's in it, it is not the only test that accompanies the t-distribution... I discovered today...
Jargon Followup: with regard to Welch test vs Student test what are the associated distributions; are they the same or different? is just the test different, but they use the same distribution for scoring? Lets step back: what is the difference between 'test', and the "score"... the test is the equation that produces a score, and the "score/value/statistic" is the point on an x-axis on a histogramic distribution. In most/all cases the distribution is related to the test via its letter... ex t-test is to t-distribution what z-test is to z-distribution.... this means both the welch t-test and the student t-test use the same t-distribution to determine a t-score/t-value/t-statistic...
Actually, I think I got information overload... we are actually not using a t-distribution, we are using a normal distribution... but I am confusing the terminology of 't-statistic' with the terminology of 'test-statistic', the later being a more general term for the results of any test regardless of distribution... (ie a t-statistic is a test-statistic associated with a t-test and a t-distribution).
Oh wow! another interesting jargon-fact: the Standard Normal Distribution N(0,1) is also called the z-distribution, so we are doing a z-test, I presume... but you tried to shield us from all the horrible jargon! I better understand and appreciate your pedagogy in this cruel world! Mr Zedstatistics in deed!
Mr. Justin Z--video 18.0: Why is V(P1-P0) the sum of V(P1) + V(P0) and not the difference of V(P1) + V(P0)
factor -1 out from V(-P0) as (-1)²
Which software creates this bubbly presentation?
prezi
Hi, I am just wondering if anyone knows why we used a T- distribution for the hypothesis test but a Z distribution for the confidence interval at 37:36?
ruclips.net/video/RkL3cG5QHbE/видео.html