Explaining the Chi-squared test
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- Опубликовано: 30 сен 2024
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The degree of freedom for the same test statistics for bigger contingency tables with I rows and J columns should be (I-1)×(J-1), for those wondering
this guy knows
I liked this video. I just would have felt very confused by what "expected" means in this context had I not already known it. I think that a good place to quickly explain it would have been when you were explaining why a 2x2 table has one degree of freedom. On the other hand it might get someone still confused to research it more themselves. On the other other hand, I am not sure where they should go to find something like that out, as most resources are not easy for a beginner to approach for math.
Wake up babe. Very Normal uploaded a new video!
Just one question
At the end when the p value is less than 5% we fail to reject the null hypothesis.
Means our drug is not effective.
Right?
😔 yeah you’re right, my company is going to need to fictionally downsize
The p-value is actually 15%, namely greater than 5%.
And yes, we don't reject the null.
good video, i was just confused with the expected table numbers, I thought to caculate this by hand any of the tables you displayed were good. I ended up learning to multiply the margins and applying the Yate's correction and that was enough replicate the result you got from R
you have no idea how long I've waited for this
Lost from 8.28; until then, fantastic especially with Claude for remediation.
Damn dude, what is the frequency of you hitting the gym? Your arms are BIG
weekdays
and ty lol
😩😩😩 10/10 training without even having to apply to the job
This is a great video, but I'd like to make two comments for everybody:
- "Chi Squared test" is an awful name, because there are many, many different statistical tests that have Chi-squared(n) as its null-distribution. As a group, let's all try to phase out the use of this terminology.
- The test presented in this video is increasingly replaced by the G-test. The test statistic in this video is an asymptotic approximation of the G-test statistic. The asymptotic distribution of the G-test is Chi-squared (which comes back to the first point).
It would ‘ve been great if you had shown how the expected frequencies under the independence assumption are calculated.
One of my favorite channels thanks a lot.
Great vid, thanks a ton!🏆
Awesome content! You should definitely do a video about survival analysis
Thanks! I do have a small bit of survival in another video about the “biggest award in statistics” but it’s definitely worth it’s own video
Shit just got real.
How do you choose between using 2 sampled t-test and chi squared test?
Are there any examples where one would be suitable and one wouldn't?
I think you mean the two-sample proportion test, the t-test is technically for continuous outcomes.
The chi-squared test (in this video) is actually equivalent to the two sample proportion test, assuming everything I did in the video. The conclusions would be the same, no matter which you use.
If you run the proportion test in R, you’ll actually see it uses the chi-squared test to calculate a p-value.
You would want to use something else if your sample size is small or isn’t mutually exclusive. A usual substitution is Fisher’s test for small sample sizes. For paired data, there’s also McNemar’s test.
@@very-normal Sorry yes, I did mean the 2 sample proportion test. Thank you for the reply.
What about chi Square goodness of fit
it kinda follows the same logic. The null hypothesis is that your data comes from some specific distribution. Your data would actually be a contingency table with one row because a goodness of fit test looks at whether or not your data conceivably comes from a given distribution. Based on this specific distribution, you can calculate expected counts. From there you calculate the statistic in the same way.
best youtuber
Thanks!
I came here for the math. Disappoint.
sorry bud