Standard error:
Is always the standard deviation divided by the square root of n.
Is a measure of the variability of a sample statistic.
Increases with bigger sample sizes.
All of the above.
A study examining the relationship between fetal X-ray exposure and a particular type of childhood blood cancer found the following odds ratio (and 95% confidence interval) for the association: 2.44 (0.95 to 6.33). This result would likely be considered:
A) clinically significant, but statistically insignificant
B) neither clinically nor statistically significant
C) both clinically and statistically significant
D) clinically insignificant, but statistically significan
SEm = SD/√n
Thank you PsyDAG. I hope you had answer before.. I answered the B!
Oh now. I was right. You are wrong!
Is a measure of the variability of a sample statistic.
The correct answer is "All of the above."
The standard error is a measure of the uncertainty or variability of a sample statistic, such as the sample mean or sample proportion. It quantifies how much the sample statistic is expected to vary from sample to sample.
To calculate the standard error, you need to divide the standard deviation of the population by the square root of the sample size (n). The standard deviation measures how much the data points in the population vary from the population mean. Dividing it by the square root of n scales it down to estimate the variability of the sample statistic.
It is important to note that as the sample size increases, the standard error also increases. This is because larger sample sizes typically provide more accurate estimates of the population parameter, resulting in smaller variability or uncertainty. Therefore, the standard error tends to decrease as the sample size gets larger.