Assuming your two variables are quantitative and you are looking to investigate the association between them, you can use a Spearman's rank correlation analysis. Though I dont use SPSS, any decent statistical package will have that analysis as an option.
I have spent all night trying to understand the concept of Sem. This is the simplest and the best video that clears all my doubts and provides the perfect definition "The statistic that measures the amount of variation of the sample mean around the true population mean " thats it !! Thanks a ton
"The standard error is used to represent the precision of the estimate of the mean" Thank you so much! Its such a simple and concise answer that I haven't heard in other videos! Why don't they just say this? lol thanks so much, I'd like to give you more thumbs ups!
I understand the explanation but what I don't have any idea is that when I run a simple regression in R, it always comes up with SE. Refering to your explanation that SE is acquired by demeaning all sampe sets' standard deviation (in your example there are 4 sample sets N=5 N=10 etc), my question is, how does R get different sample sets to calculate SE from varied samples mean, whereas I only have one dataset or one sample set. Does R simulate the repeated sampling and resampling in background?
I finally understood these concepts now. I never understood this in med school, and I've been struggling with biostatistics mentioned in journals for years now. Thanks a lot!!! :) Also, I absolutely love your voice!
The formula for the standard error looks like it’s just a guess, “rule of thumb” It’s basically just reducing the standard deviation as the sample size increases. My question is, are we simply just Assigning a value to the statistic According to the sample size?
Exactly my same question, why is that sqrt of n down there? In other words, why taking something that is already a mean between all the observation and basically dividing this quantity again for all the observation? What that value, which comes from a single sample, which is basically the mean of a mean of distances from the mean, tell us about the population?
When you are comparing two populations, you are often comparing their means. The statistical tests used to compare means (t-test, ANOVA, etc) use the measure of uncertainty around your estimation of the mean (SE) as part of the calculation in determining if the two means are significantly different from one another (the more uncertainty there are in the estimates, the harder it would be to declare them significantly different). Because the distribution of sampling means (the distribution of the means of all possible samples taken of size n) becomes more narrow as n increase, the probability that any sample mean more truly represents the true population mean increases (therefore the SE is smaller in these calculations). This principle is called the law of large numbers. The formulas are based on mathematical properties.
If you have 5 treatments and each has 5 replicates, what is the appropriate way of reporting SEM, (i) calculate SEM based on 25 measurement (5x5) (ii) calculate SEM separately for the 5 treatments and do average SEM
Each treatment will have it's own mean and SEM, so in this case the SEM would be calculated with N=5. The idea of an "average SEM" comes into play when you are using an Analysis of Variance (ANOVA), where one of the assumptions is equality of variances, therefore all of the treatments should have the same estimate for the variance, and if balanced (same number of replicates for all treatments) the SEMs would also be the same.
Hi! When my question asks me to find the point estimates for the population standard deviation, so which do i use? The standard deviation value or standard error value? Thank you so much for the video.
You would use the standard deviation that you calculated from your sample. That is the best estimate you have of the true population standard deviation.
@@biostatisticsbydesign thank u but if so then why do we need to find the CI from the Z tables? Why not take 2 SE to be the CI as 2 SE contain 95% of the means ie we are 95% sure that the true mean will lie within 2 SE.
Mam iam not able to understand why do we divide standard deviation of sample by √n. Iam not able to see logic behind it. Please explain it it would be a great help.
The standard error (SE) measures how accurately a sample represents a population. It tells you how different the population mean is likely to be from a sample mean. The standard error is the average error that would be expected in using a sample mean as an estimate of the real population mean. The standard error is inversely proportional to the sample size. It tends to zero as the number of observations in the sample increases, so the sample represents the population more accurately (the "law of large numbers"). The standard deviation describes the variation between observations/individuals within a sample or population, and is an unbiased estimator, meaning that on average, the SD will not change as sample size increases. This is why we divide by the square root of N to estimate the SE.
How can I run 2 non-parametric variables analysis using SPSS?
Assuming your two variables are quantitative and you are looking to investigate the association between them, you can use a Spearman's rank correlation analysis. Though I dont use SPSS, any decent statistical package will have that analysis as an option.
I have spent all night trying to understand the concept of Sem. This is the simplest and the best video that clears all my doubts and provides the perfect definition "The statistic that measures the amount of variation of the sample mean around the true population mean " thats it !! Thanks a ton
I'm so glad this was helpful for you!
"The standard error is used to represent the precision of the estimate of the mean" Thank you so much! Its such a simple and concise answer that I haven't heard in other videos! Why don't they just say this? lol thanks so much, I'd like to give you more thumbs ups!
Thank you so much!
"Precision of estimate of the mean" thank u so much for this clear and short definition. Each word in it is full of sense
You're very welcome!
I understand the explanation but what I don't have any idea is that when I run a simple regression in R, it always comes up with SE. Refering to your explanation that SE is acquired by demeaning all sampe sets' standard deviation (in your example there are 4 sample sets N=5 N=10 etc), my question is, how does R get different sample sets to calculate SE from varied samples mean, whereas I only have one dataset or one sample set. Does R simulate the repeated sampling and resampling in background?
I finally understood these concepts now. I never understood this in med school, and I've been struggling with biostatistics mentioned in journals for years now. Thanks a lot!!! :)
Also, I absolutely love your voice!
This is such a great compliment. Very much appreciated, thank you!!
The formula for the standard error looks like it’s just a guess, “rule of thumb”
It’s basically just reducing the standard deviation as the sample size increases.
My question is, are we simply just Assigning a value to the statistic According to the sample size?
Exactly my same question, why is that sqrt of n down there?
In other words, why taking something that is already a mean between all the observation and basically dividing this quantity again for all the observation? What that value, which comes from a single sample, which is basically the mean of a mean of distances from the mean, tell us about the population?
When you are comparing two populations, you are often comparing their means. The statistical tests used to compare means (t-test, ANOVA, etc) use the measure of uncertainty around your estimation of the mean (SE) as part of the calculation in determining if the two means are significantly different from one another (the more uncertainty there are in the estimates, the harder it would be to declare them significantly different). Because the distribution of sampling means (the distribution of the means of all possible samples taken of size n) becomes more narrow as n increase, the probability that any sample mean more truly represents the true population mean increases (therefore the SE is smaller in these calculations). This principle is called the law of large numbers. The formulas are based on mathematical properties.
If you have 5 treatments and each has 5 replicates, what is the appropriate way of reporting SEM, (i) calculate SEM based on 25 measurement (5x5) (ii) calculate SEM separately for the 5 treatments and do average SEM
Each treatment will have it's own mean and SEM, so in this case the SEM would be calculated with N=5. The idea of an "average SEM" comes into play when you are using an Analysis of Variance (ANOVA), where one of the assumptions is equality of variances, therefore all of the treatments should have the same estimate for the variance, and if balanced (same number of replicates for all treatments) the SEMs would also be the same.
Hi! When my question asks me to find the point estimates for the population standard deviation, so which do i use? The standard deviation value or standard error value? Thank you so much for the video.
You would use the standard deviation that you calculated from your sample. That is the best estimate you have of the true population standard deviation.
So can we say thatcSE is the SD of the means of samples around the mean of the means?
Yes!
@@biostatisticsbydesign thank u but if so then why do we need to find the CI from the Z tables? Why not take 2 SE to be the CI as 2 SE contain 95% of the means ie we are 95% sure that the true mean will lie within 2 SE.
The equation for SE has mu (population mean) but we don’t know mu in most cases. How do we calculate SE from x-bar (sample mean)?
Where ever we do not know the sd of population we can use the sample sd instead
Your mean and SD calculated from your sample are your best estimate of the population parameters, therefore you would use those to calculate your SE.
Thanks for your video! Is there a difference between "standard error" (SE) and "standard error of the mean" (SEM), or are these the same thing?
Nope. SE and SEM refer to the same thing.
Mam iam not able to understand why do we divide standard deviation of sample by √n. Iam not able to see logic behind it. Please explain it it would be a great help.
The standard error (SE) measures how accurately a sample represents a population. It tells you how different the population mean is likely to be from a sample mean. The standard error is the average error that would be expected in using a sample mean as an estimate of the real population mean. The standard error is inversely proportional to the sample size. It tends to zero as the number of observations in the sample increases, so the sample represents the population more accurately (the "law of large numbers"). The standard deviation describes the variation between observations/individuals within a sample or population, and is an unbiased estimator, meaning that on average, the SD will not change as sample size increases. This is why we divide by the square root of N to estimate the SE.
Great video! Thanks a lot!
Thank you, you're very welcome!!
Really helpful. Carry on.
Glad to hear it! I've been slacking lately but need to get back on it!
Thank you for this video
You're welcome!
Very helpful. Thank you!
I'm glad it was helpful!
amazing video! thanks so much!
Thank you. You are very welcome!
Quite helpful thanks!
You're very welcome!
Great video THANKYOU 😇😇
You are very welcome!
Well done
Thank you!
Thanks
You are very welcome. :)