Can't agree more ,don't know why they like to make everything harder than it is I used to hate these statistical things but after searching and learning from an outsource i really liked it and i started thinking about working as a data analyst
I'm a grade 10 high school student from the Philippines and we had to make an research. We need to use the T test in our research thank you so much I learned so mich🙏
We deal with population groups and of recent are also combining qualitative and quantitative data analysis in our work. The t-Test course has come in handy for us because we refreshed our minds about many of the principles. Thanks.
I am currently having to do some T-Test analysis as part of my MSc. For someone who has never encountered statistics before (at least not for a while), you have really helped me to understand this process. Many thanks.
Amazing visuals, but please stop switching between examples... First it's patients and blood pressure, then it's chcolate and grams, then patients and pain relief...
Hello. I just want a simple suggestion as to what type of statistics (Anova, chi-square, etc.) I should use to determine my goal below. I am truly confused yet optimistic for someone generous out there, and I would greatly appreciate any additional comments or suggestions to clarify or simplify my statement or claim here. Thank you in advance. "Among the randomly selected senior students in the eight (4 public and 4 private) US south-west states (California, Arizona, New Mexico, and Texas), their responses are unanimous (strongly agree, agree, neutral, disagree, strongly disagree) as regards participating in home gardening rather than school gardening."
Is it possible to do a t test on football stats like goals and assists based on certain factors like women and men world Cup or something like football after and during covid
Hmm, that would have to be looked at more closely, but the question of whether there is a difference between the number of goals scored in a match by men and women can already be tested with a t-test. You only have to check whether the data are normally distributed! If not, you could use the Mann-Whitney U-test. Please have a look here: ruclips.net/video/LcxB56PzylA/видео.html
@@blessedmusakaruka1235 If I understand it correctly, you would use a Chi2 Test in this case, please have a look here: ruclips.net/video/EjtXk-yEK6w/видео.html
Hi, I have 10 Pairs of Land Surface Temperature (LST) data from 10 sites for two years. If I want to conclude that overall T-Test suggests there are significant difference between the two years, do all 10 pairs need to be significant individually? or if majority sites show significant difference, I can conclude that the overall result is significant?
Hi : ), when you're analyzing data from 10 different sites for two years and aiming to use a t-test to determine if there's a significant difference in the Land Surface Temperature (LST) between the two years, it's important to understand the nuances of statistical significance and how it applies to your dataset. Whether each of the 10 pairs must individually show significance or if a majority is enough to conclude an overall significant difference depends on your analysis approach and how you structure your hypothesis testing. Here are some key points to consider: Individual vs. Combined Analysis: If you're conducting individual t-tests for each of the 10 sites, then each test assesses the significance of the difference in LST between the two years for that specific site. A significant result in this context only applies to the site tested. You cannot automatically infer an overall significant difference for all sites unless you're aggregating the data or results in a statistically valid way. Aggregate Data Analysis: An alternative approach might be to aggregate the data from all sites and perform a single t-test to compare the years. This method would give you an overall picture but would also assume that all sites are comparable or have been appropriately normalized to account for site-specific differences. This aggregated analysis would allow you to make a generalized conclusion about the difference in LST between the two years across all sites. Majority of Sites Showing Significance: Simply having a majority of sites showing a significant difference does not automatically validate an overall conclusion of significance across all sites, especially if each site is analyzed independently. The variation in significance across sites could be due to various factors, including environmental, geographical, or methodological differences. Meta-analysis or Combined Statistical Approach: If individual analyses are preferred, you might consider a meta-analytical approach or a combined statistical test that takes into account the results from the individual tests. This can help you draw a more comprehensive conclusion that accounts for the variability and significance observed across different sites. Adjusting for Multiple Comparisons: When performing multiple t-tests, one for each site, you're increasing the risk of Type I error (false positives). To account for this, you might need to adjust your significance threshold using methods like the Bonferroni correction, which involves dividing your alpha level by the number of tests performed, or other corrections designed for multiple comparisons. In conclusion, to assert that there's an overall significant difference in LST between the two years based on your data from 10 sites, you need to carefully consider how you structure your analysis. If your goal is to draw a conclusion about all sites collectively, consider aggregating the data for a single analysis or employing a statistical method that can combine the results from individual site analyses. Always be mindful of the implications of multiple testing and the need for appropriate adjustments to your significance criteria. Regards Hannah
This isn't valid. The most important part of p-value is that its threshold must be defined before the study. And typically the threshold 0.05 is chosen, but *why* ? The typical threshold of 0.05 is completely arbitrary, unscientific, and it means of 100 studies we would expect 5 with the wrong result. It doesn't mean the result is statistically significant, it means the result is considered statistically significant, even if it really isn't.
Great video! My only critique/suggestion might be that it would also have been nice to see the actual math. That might be beyond where you wanted to go with this video, so maybe it’s a “part 2” or “Advanced” follow on video ;-). Anyhow, thanks!
Thank you so much for the very good and useful presentations I really loved it !!! Can we use these slides in our presentations? or they are copyrighted materials?
If you like, please find our e-Book here: datatab.net/statistics-book 😎
2a
Much clearer than my university. What am I paying for?!
Many thanks : )
fkn facts
the degree
I am wasting my money in University 😔
Can't agree more ,don't know why they like to make everything harder than it is I used to hate these statistical things but after searching and learning from an outsource i really liked it and i started thinking about working as a data analyst
just rizzed up a girl by teaching her this, thank you so much
heres the real reason for learning this hahah
My man
Ma man. 🤜🏻
😂😂
best t-test explanation i’ve ever seen (and i’ve seen a lot)! congrats and thank you!
Textbooks writers need to watch this video. Thank you ma’am for the video😍😍😍😍😍
This video literally saves me from my finals. Thank you for your efforts.
Happy to help!
are you in college or grad or what?
Thanks so much! Finally have a clear understanding of t-test after so long.....
Glad it helped!
This is the best explanation I’ve ever come across… I’ve even published research papers using t test but this really deepened my understanding
Very nicely and succinctly presented. What a great way to refresh knowledge one acquired over 40 years ago.
Glad it was helpful and many thanks for your nice feedback! Regards Hannah
I'm a grade 10 high school student from the Philippines and we had to make an research. We need to use the T test in our research thank you so much I learned so mich🙏
Many thanks for your feedback!!! Regards Hannah
Thank you for posting this! Very easy to follow along
Glad you enjoyed it!
you're making biostatistics so easy !!!! THANK YOU !
We deal with population groups and of recent are also combining qualitative and quantitative data analysis in our work. The t-Test course has come in handy for us because we refreshed our minds about many of the principles. Thanks.
Thanks for saving me the day before my exams.
Thanks for the clearer explanation
Glad it was helpful! Regards hannah
I need to focus on my RUclips class , much more than I do to my university,
Wow thank you.
: )
no cap, learned more here than in my weekly 2hrs lecture and labs
This video is so informative, highly recommended.
really thank you for making this video
It's my pleasure! Thanks for your comment!
I am currently having to do some T-Test analysis as part of my MSc. For someone who has never encountered statistics before (at least not for a while), you have really helped me to understand this process. Many thanks.
Excellent explanation.
Glad it was helpful and thanks for the nice feedback! Regards, Hannah
Too good explanation ma'am
You are too much. Very detailed and clear. Thanks
Glad it was helpful!
@@datatab Have you please written a statistics book or any other helpful book on probability and statistics?
Thank you for the video, now I understand it much better (and clearer).❤
Excellent video and so pedagogical.😍
Glad you enjoyed it! Many thanks!
superb no other words
Glad it was helpful and many thanks for your feedback! Regards Hannah
I loved the video. Thanks a billion, it was sooo clear.
Just studying before exam 😅. thank you so much for Posting it in such an easy way
Thank you so much, your videos ar so amazing
Thank you! It is very clear
thsnk you so much for this video
Excellent Explanation
Most useful. Danke!
Thanks you are welcome : )
this is great thank you for making this
Glad you liked it!
U r really my life saver😅❤
Amazing visuals, but please stop switching between examples... First it's patients and blood pressure, then it's chcolate and grams, then patients and pain relief...
very useful, thanks
Hello. I just want a simple suggestion as to what type of statistics (Anova, chi-square, etc.) I should use to determine my goal below. I am truly confused yet optimistic for someone generous out there, and I would greatly appreciate any additional comments or suggestions to clarify or simplify my statement or claim here. Thank you in advance.
"Among the randomly selected senior students in the eight (4 public and 4 private) US south-west states (California, Arizona, New Mexico, and Texas), their responses are unanimous (strongly agree, agree, neutral, disagree, strongly disagree) as regards participating in home gardening rather than school gardening."
thanks
Is it possible to do a t test on football stats like goals and assists based on certain factors like women and men world Cup or something like football after and during covid
Hmm, that would have to be looked at more closely, but the question of whether there is a difference between the number of goals scored in a match by men and women can already be tested with a t-test. You only have to check whether the data are normally distributed! If not, you could use the Mann-Whitney U-test. Please have a look here: ruclips.net/video/LcxB56PzylA/видео.html
@@datatab OK Thank you very much
@datatab how about for students picking courses for example boys and girls chosen classes
@@blessedmusakaruka1235 If I understand it correctly, you would use a Chi2 Test in this case, please have a look here: ruclips.net/video/EjtXk-yEK6w/видео.html
JazakAllah
Hi, I have 10 Pairs of Land Surface Temperature (LST) data from 10 sites for two years. If I want to conclude that overall T-Test suggests there are significant difference between the two years, do all 10 pairs need to be significant individually? or if majority sites show significant difference, I can conclude that the overall result is significant?
Hi : ), when you're analyzing data from 10 different sites for two years and aiming to use a t-test to determine if there's a significant difference in the Land Surface Temperature (LST) between the two years, it's important to understand the nuances of statistical significance and how it applies to your dataset. Whether each of the 10 pairs must individually show significance or if a majority is enough to conclude an overall significant difference depends on your analysis approach and how you structure your hypothesis testing. Here are some key points to consider:
Individual vs. Combined Analysis: If you're conducting individual t-tests for each of the 10 sites, then each test assesses the significance of the difference in LST between the two years for that specific site. A significant result in this context only applies to the site tested. You cannot automatically infer an overall significant difference for all sites unless you're aggregating the data or results in a statistically valid way.
Aggregate Data Analysis: An alternative approach might be to aggregate the data from all sites and perform a single t-test to compare the years. This method would give you an overall picture but would also assume that all sites are comparable or have been appropriately normalized to account for site-specific differences. This aggregated analysis would allow you to make a generalized conclusion about the difference in LST between the two years across all sites.
Majority of Sites Showing Significance: Simply having a majority of sites showing a significant difference does not automatically validate an overall conclusion of significance across all sites, especially if each site is analyzed independently. The variation in significance across sites could be due to various factors, including environmental, geographical, or methodological differences.
Meta-analysis or Combined Statistical Approach: If individual analyses are preferred, you might consider a meta-analytical approach or a combined statistical test that takes into account the results from the individual tests. This can help you draw a more comprehensive conclusion that accounts for the variability and significance observed across different sites.
Adjusting for Multiple Comparisons: When performing multiple t-tests, one for each site, you're increasing the risk of Type I error (false positives). To account for this, you might need to adjust your significance threshold using methods like the Bonferroni correction, which involves dividing your alpha level by the number of tests performed, or other corrections designed for multiple comparisons.
In conclusion, to assert that there's an overall significant difference in LST between the two years based on your data from 10 sites, you need to carefully consider how you structure your analysis. If your goal is to draw a conclusion about all sites collectively, consider aggregating the data for a single analysis or employing a statistical method that can combine the results from individual site analyses. Always be mindful of the implications of multiple testing and the need for appropriate adjustments to your significance criteria.
Regards
Hannah
@@datatab Thank you for such detailed reply. It helps a lot!
@@TarannumKhandoker You are welcome!
Amazing!!
Thanks!! : ) Regards Hannah
amazing
esto es oro
good
Thanks : )
Weimann Ridge
😢😢😢😢😢God bless you 🎉
i love you
This isn't valid. The most important part of p-value is that its threshold must be defined before the study. And typically the threshold 0.05 is chosen, but *why* ?
The typical threshold of 0.05 is completely arbitrary, unscientific, and it means of 100 studies we would expect 5 with the wrong result.
It doesn't mean the result is statistically significant, it means the result is considered statistically significant, even if it really isn't.
here a night before the exam after successfully wasting 4000 dollars
Mam can u teach online
Outstanding job!
Crystal, precise.... Classic German!
Great video! My only critique/suggestion might be that it would also have been nice to see the actual math. That might be beyond where you wanted to go with this video, so maybe it’s a “part 2” or “Advanced” follow on video ;-). Anyhow, thanks!
Really we need a professor like you in our university
Karen Serdeña - International Rice Research Institute (IRRI)
Thank you so much for the very good and useful presentations
I really loved it !!!
Can we use these slides in our presentations? or they are copyrighted materials?
Extreamly helpful video , concepts are explained very well . Thank you so much mam
As a student, can i use datatab in free?
This is a fantastic video. Thank you.
Glad you enjoyed it and thanks for your feedback! Regards, Hannah
thank you so much for making simplier
You are welcome!
amazing and easy to understand.
Glad it was helpful! Thanks!
I'm curious as to why the difference becomes less meaningful when the data is more scattered.
can someone explain me why the paired sample t-test should be equal to zero?
Studying by this for my finals 😂 wish me luck 🤞
what if the data has the same value? will the rank is the same as well for spearman?
More confused
Y
Is t-test the correst test to analyse push and pull factors of migration?
Thank you for this video.
Beautiful accent
Highly informative
Excellent !
Thank you! Cheers!
Thank you so much ma'am
fantastico! veramente molto chiaro 🔝
💯
Thanks!
Thank you for this