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Two Proportions Hypothesis Test - Excel quiz

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  • What is the null hypothesis (H0) when testing two population proportions?
    The null hypothesis is that the two proportions are equal, or H0: p1 = p2.
  • How do you define the alternative hypothesis (Ha) if you suspect the first proportion is less than the second?
    The alternative hypothesis is Ha: p1 < p2.
  • What Excel function can you use to count the number of 'yes' responses in a data range?
    You can use the COUNTIF function, e.g., =COUNTIF(range, "yes").
  • How do you calculate the sample proportion (p̂) in Excel?
    Divide the number of successes (x) by the sample size (n), e.g., =x/n.
  • What is the formula for the pooled proportion (p̄) in a two-proportion z-test?
    The pooled proportion is (x1 + x2) / (n1 + n2).
  • How do you calculate q̄ (the complement of the pooled proportion) in Excel?
    Subtract the pooled proportion from 1, e.g., =1 - p̄.
  • What is the numerator in the z-score formula for two proportions?
    The numerator is the difference between the sample proportions: p̂1 - p̂2.
  • How do you calculate the denominator of the z-score formula for two proportions?
    Take the square root of [p̄ × q̄ × (1/n1 + 1/n2)].
  • Which Excel function converts a z-score to a left-tail p-value?
    Use =NORM.S.DIST(z, TRUE) to get the cumulative left-tail probability.
  • What does it mean if your p-value is greater than your alpha level (e.g., 0.05)?
    It means you fail to reject the null hypothesis; there is not enough evidence for the alternative.
  • If both sample sizes (n1 and n2) are equal, how does this affect the denominator in the z-score formula?
    The terms p̄ × q̄ / n1 and p̄ × q̄ / n2 will be equal, simplifying the calculation.
  • What is the conclusion if you fail to reject the null hypothesis in a two-proportion test?
    There is not enough evidence to support the claim that the proportions are different as stated in Ha.
  • Why is it helpful to break down the z-score formula into parts when using Excel?
    It reduces the chance of errors and makes the calculation easier to follow.
  • What is the significance level (alpha) commonly used in hypothesis testing?
    A common alpha level is 0.05, representing a 5% risk of Type I error.
  • What does the p-value represent in the context of a two-proportion hypothesis test?
    The p-value is the probability of observing a test statistic as extreme as, or more extreme than, the one calculated, assuming the null hypothesis is true.