Simple, easily to digest and break down and for utmost benefits.... thank you ma'am I wish you were my professor back in the medical university. Keep it up, Will you? 👏👏👏👏👏👏👏👏👏
I like the videos a lot, I have learned a lot. I would love for you to make a video on what sample size to take for a T test or ANOVA. Or rather how many repetitions should I use?
Hey, you said the normality assumption becomes less important as the sample size increases. From what I have seen in the internet, if the sample size > 30 and the data is not normal then we can still use a T-test or an ANOVA test as they are more robust, unless the distribution is very skewed. We can use T-tests or ANOVA test because: 1. We can use Box-cox transformation if the data is not extremely skewed. Is my understanding correct, or there is something else I am missing? If you could clarify an another question, I would really appreciate it. Let's say we want to check if the heights differ between two groups significantly. We draw 10 persons from each group and measure their height. Theoritcally we know that heoghts are normally distributed, but when we look at the sampled data (both groups) they do not follow a normal distribution. So in this case should be use a T-test or Mann Whitney U-test. Finally, why is that Hypothesis tests that make assumptions (parametric tests) are more powerful than non-parametric tests that do not make assumptions, when the data satisfies the assumptions of parametric tests?
![ILLSLICK - KILLSHOT REMIX [FULL VERSION]](http://i.ytimg.com/vi/RIK8RrqIAzE/mqdefault.jpg)
If you like, please find our e-Book here: datatab.net/statistics-book 📔
Simple, easily to digest and break down and for utmost benefits.... thank you ma'am I wish you were my professor back in the medical university.
Keep it up, Will you?
👏👏👏👏👏👏👏👏👏
Many thanks for your nice feedback! Regards Hannah
Excellent. Congrats for pedagogical explained.
Thanks a lot! Regards Hannah
I appreciate your effort and time you spend in making this video. Nicely explained
Fantastic visuals, great way to explain the topic.
Wonderfully pedagogical❤️🥰❤️
Many thanks Per : ) Regards Hannah
I like the videos a lot, I have learned a lot. I would love for you to make a video on what sample size to take for a T test or ANOVA. Or rather how many repetitions should I use?
superb explanation 🎉🎉 thank you so much🙏🙏
I love the way you simplify statistics. Would you mind doing something on propensity score matching? I'm not certain that the test is on datatab yet.
Thank you!
You're welcome! Regards Hannah
Can you please do a video on principal component analysis?
Hey, you said the normality assumption becomes less important as the sample size increases. From what I have seen in the internet, if the sample size > 30 and the data is not normal then we can still use a T-test or an ANOVA test as they are more robust, unless the distribution is very skewed.
We can use T-tests or ANOVA test because:
1. We can use Box-cox transformation if the data is not extremely skewed.
Is my understanding correct, or there is something else I am missing?
If you could clarify an another question, I would really appreciate it.
Let's say we want to check if the heights differ between two groups significantly. We draw 10 persons from each group and measure their height.
Theoritcally we know that heoghts are normally distributed, but when we look at the sampled data (both groups) they do not follow a normal distribution. So in this case should be use a T-test or Mann Whitney U-test.
Finally, why is that Hypothesis tests that make assumptions (parametric tests) are more powerful than non-parametric tests that do not make assumptions, when the data satisfies the assumptions of parametric tests?
😀🤩🤩🤩🤩🙂😊
: )