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Using the Central Limit Theorem, it is found that the standard deviation is of 0.0971.
What does the Central Limit Theorem states?
- It states that for a proportion p in a sample of size n, the sampling distribution of sample proportion is approximately normal with mean [tex]\mu = p[/tex] and standard deviation [tex]s = \sqrt{\frac{p(1 - p)}{n}}[/tex], as long as [tex]np \geq 10[/tex] and [tex]n(1 - p) \geq 10[/tex].
- It also states that when two variables are subtracted, the standard deviation is the square root of the sum of the variances.
In this problem, for each sample, the standard error is given by:
[tex]s_I = \sqrt{\frac{0.25(0.75)}{50}} = 0.0612[/tex]
[tex]s_{II} = \sqrt{\frac{0.35(0.65)}{40}} = 0.0754[/tex]
Hence, for the distribution of differences, it is given by:
[tex]s = \sqrt{s_I^2 + s_{II}^2} = \sqrt{0.0612^2 + 0.0754^2} = 0.0971[/tex]
More can be learned about the Central Limit Theorem at https://brainly.com/question/24663213
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