Are the statements about the confidence interval correct?

In summary: We're applying a z-value, which can only apply if the standard deviation $\sigma_x$ is given.But apparently it's not, since we have an $s_x$, which would typically be calculated from a sample.In that case I think we're supposed to apply a $t$-score instead of a $z$-score, aren't we? (Wondering)
  • #1
mathmari
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Hey! :eek:

We have a 90%-confidence interval. I want to check if the following statements are correct.

1. If double the sample, the possibility that the value that we are looking for is out of the confidence interval is smaller.

2. The bigger the standard error, the smaller the confidence interval. Since the confidence interval is $\left (\overline{x}- Z_{a/2}\cdot s_x, \overline{x}+ Z_{a/2}\cdot s_x\right )$, where $s_x$ is the standard error, I think that the second statement is wrong and it should be that the bigger the standard error, the bigger the confidence interval.
Is this correct? (Wondering)

What about the first statement? (Wondering)
 
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  • #2
It's still a $90\%$ confidence interval, right? With double the sample size, your confidence interval will shrink in absolute width, but it will still be calculated on the basis of $90\%$ confidence. The probabilities will not change, just the interval size.
 
  • #3
Ackbach said:
It's still a $90\%$ confidence interval, right? With double the sample size, your confidence interval will shrink in absolute width, but it will still be calculated on the basis of $90\%$ confidence. The probabilities will not change, just the interval size.

I see! Thank you very much! (Smile)
 
  • #4
Erm...
We're applying a z-value, which can only apply if the standard deviation $\sigma_x$ is given.
But apparently it's not, since we have an $s_x$, which would typically be calculated from a sample.
In that case I think we're supposed to apply a $t$-score instead of a $z$-score, aren't we? (Wondering)

Furthermore, you mention a so called standard error, but that means we're talking about the standard deviation of the mean of a sample, typically defined as $SE=s_{\bar x}=\frac{s_x}{\sqrt n}$.

This is about the standard deviation of the sample mean. Are we on the same page here? (Wondering)
 

Related to Are the statements about the confidence interval correct?

1. What is a confidence interval?

A confidence interval is a range of values that is likely to contain the true population parameter with a certain level of confidence. It is used to estimate the range of values within which the true population parameter is likely to fall.

2. How is a confidence interval calculated?

A confidence interval is calculated using the sample data and the desired level of confidence. The formula for calculating a confidence interval is: sample mean ± z*(standard error), where z is the critical value based on the desired level of confidence and standard error is the standard deviation of the sample divided by the square root of the sample size.

3. What does the confidence level represent in a confidence interval?

The confidence level in a confidence interval represents the percentage of times that the true population parameter would fall within the calculated range. For example, a confidence level of 95% means that if we were to repeat the experiment multiple times, we would expect the true population parameter to fall within the calculated range 95% of the time.

4. Can a confidence interval be used to make a definitive statement about the true population parameter?

No, a confidence interval cannot be used to make a definitive statement about the true population parameter. It only provides a range of values within which the true population parameter is likely to fall. The sample data used to calculate the confidence interval may not be representative of the entire population, so there is always a margin of error in the estimate.

5. How does sample size affect the width of a confidence interval?

The larger the sample size, the narrower the confidence interval will be. This is because as the sample size increases, the standard error decreases, resulting in a smaller margin of error. A smaller margin of error means the confidence interval will be more precise and closer to the true population parameter.

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