[Solved] Which of the following is NOT true about the standard error of a statistic?

McqMate

Q.	Which of the following is NOT true about the standard error of a statistic?
A.	The standard error measures, roughly, the average difference between the statistic and the population parameter.
B.	The standard error is the estimated standard deviation of the sampling distribution for the statistic.
C.	The standard error can never be a negative number.
D.	The standard error increases as the sample size(s) increases.
Answer» D. The standard error increases as the sample size(s) increases.

926

0

Do you find this helpful?

1

View all MCQs in

Total Quality Management (TQM)

Discussion

No comments yet

Related MCQs

The error deviations within the residual sum of squares (SSE) statistic measure distances:
The South African government is concerned about the high incidence of HIV/AIDS in South Africa. They wish to estimate the true number of people in South Africa who are HIV positive. A random sample of 5000 people was tested and 1980 of them were HIV positive. What is the statistic?
You obtained a significant test statistic when comparing three treatments in a one-way ANOVA. In words, how would you interpret the alternative hypothesis H(a)?
Student's t-statistic is applicable in case of:
A null hypothesis is rejected if the value of a test statistic lies in the:
To determine whether the test statistic of ANOVA is statistically significant, it can be compared to a critical value. What two pieces of information are needed to determine the critical value?
What statistic is used to check the significance of the Kruskal-Wallis test?
To determine whether the test statistic of ANOVA is statistically significant, it can be compared to a critical value. What two pieces of information are needed to determine the critical value?
The standard error of regression is
You conduct a hypothesis test and you observe values for the sample mean and sample standard deviation when n = 25 that do not lead to the rejection of Ho (null hypothesis). You calculate a p-value of 0.0667. What will happen to the p-value if you observe the same sample mean and standard deviation for a sample n>25?