Is a higher standard error better?
What the standard error gives in particular is an indication of the likely accuracy of the sample mean as compared with the population mean. The smaller the standard error, the less the spread and the more likely it is that any sample mean is close to the population mean. A small standard error is thus a Good Thing.
What does it mean when standard error is high?
The standard error tells you how accurate the mean of any given sample from that population is likely to be compared to the true population mean. When the standard error increases, i.e. the means are more spread out, it becomes more likely that any given mean is an inaccurate representation of the true population mean.
What does a high standard error for a coefficient mean?
A high standard error (relative to the coefficient) means either that 1) The coefficient is close to 0 or 2) The coefficient is not well estimated or some combination. “High” by itself doesn’t really have a set meaning (you can change the SE by changing the unit – measure in miles instead of microns and the SE will be tiny).
Why are there so many high standard errors?
As for “high standard errors”, model ML SE is the reliability of parameter estimates based upon the data, not a measure of the reliability of your data per se. The question is if the interpretation of the output makes sense given your hypothesis and understanding of the subject.
What is a ” high ” standard error ( in logistic regression?
What is a “high” standard error (in logistic regression)? I can’t find in any statistics book what would start to be considered a large standard error of a regression coefficient.
Why are there high standard errors in ml SE?
If in doubt and your data are all categorical, it can be appropriate and informative to check your results against an exact test or chi square. As for “high standard errors”, model ML SE is the reliability of parameter estimates based upon the data, not a measure of the reliability of your data per se.