How Do You Calculate Sample Size for a T-Test with a 95% Confidence Interval?

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In summary, the conversation is about solving question 12 which involves using the hypothesis testing 1 sample t test formula to calculate a confidence interval of a mean. The given values are X=101.6, U=100, s=15, and we are looking for n. The formula is x-u/(s/✓n) and the 95% confidence level corresponds to t=1.96. The equation to solve is 100 = 101.6 -1.96 \frac{15}{\sqrt{n}} to find the value of n.
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markosheehan
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i am stuck on question 12

I am trying to use the hypothesis testing 1 sample t test formula .

the right answer is 338 but I can not get this
 

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I think this question has to do with the lower bound of the confidence interval since the mean of the sample is above 100. What is the formula you've been given to calculate a confidence interval of a mean? What values do we already have that fit into that equation?
 
  • #3
So the forumula is x-u/(s/✓n). Where X is the value 101.6

U is the mean which is 100,s is the standard deviation which is 15 , n is the size of the sample which is what we are looking for. What I'm not sure is what I am supposed to equal this to. I know it's something to do with the 5% level of confidence, but that forumula gives its answer as a z value.
 
  • #4
markosheehan said:
So the forumula is x-u/(s/✓n). Where X is the value 101.6

U is the mean which is 100,s is the standard deviation which is 15 , n is the size of the sample which is what we are looking for. What I'm not sure is what I am supposed to equal this to. I know it's something to do with the 5% level of confidence, but that forumula gives its answer as a z value.

On the right track!

So here's our confidence interval formula: \(\displaystyle \overline{x} \pm t^{*} \frac{s}{\sqrt{n}}\).

Like you wrote, we just care about the lower bound so we can ignore the upper bound for now. The $t$ here corresponds to the 95% confidence level. Usually we say when $n>30$ we can use the normal distribution which would make $t=1.96$. If we plug that in we get:

\(\displaystyle 100 = 101.6 -1.96 \frac{15}{\sqrt{n}}\)

Now we want to solve for $n$.
 

FAQ: How Do You Calculate Sample Size for a T-Test with a 95% Confidence Interval?

What is a t-test hypothesis test?

A t-test hypothesis test is a statistical method used to determine whether there is a significant difference between the means of two groups. It is often used to compare the means of a control group and an experimental group in a study.

How does a t-test work?

A t-test works by calculating the difference between the means of two groups and comparing it to the variability within each group. It then determines the probability of obtaining such a difference by chance alone. If this probability is below a predetermined value (usually 0.05 or 0.01), the difference is considered statistically significant.

What are the assumptions of a t-test?

The assumptions of a t-test include:

  • The data is normally distributed within each group
  • The two groups being compared have equal variances
  • The observations within each group are independent
  • The data is measured on an interval or ratio scale
If these assumptions are not met, alternative statistical tests may need to be used.

When should a t-test be used?

A t-test should be used when:

  • There are two independent groups being compared
  • The data is normally distributed within each group
  • The data is measured on an interval or ratio scale
Additionally, a t-test can be used when comparing the means of the same group at two different time points (paired t-test) or when comparing the means of more than two groups (ANOVA).

What do the results of a t-test mean?

The results of a t-test indicate whether there is a statistically significant difference between the means of the two groups being compared. If the p-value is less than the predetermined significance level (usually 0.05 or 0.01), it means that the observed difference is unlikely to have occurred by chance alone. This suggests that there is a true difference between the means of the two groups. If the p-value is greater than the significance level, the difference is not considered statistically significant and may have occurred by chance.

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