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  1. 18 hours ago · The chi-square test is used to determine if there's a significant association between categorical variables. It compares observed frequencies (what you actually count) to expected frequencies (null hypothesis prediction), helping researchers assess whether differences are due to chance or a real relationship in the population. Reviewer. Author.

  2. 4 days ago · Significance Level (Alpha): The significance level, often denoted as alpha (α), is the probability of rejecting the null hypothesis when it is actually true (Type I error). Commonly set at 0.05, lowering alpha requires a larger sample size to maintain the same power.

  3. 5 days ago · A hypothesis test of the mean of a normal distribution is a test to decide whether there is statistically significant evidence that the mean has changed from a known previous value. This test is for a population that is Normally distributed and has known variance σ 2.

  4. 5 days ago · The significance level, α, dictates how demanding the test is. If the significance level is 10%, or α = 0.1, then we’re looking to see whether the probability of the observed result (under the old probability) is less than 0.1.

  5. 3 days ago · A p-value less than 0.01 indicates strong statistical significance in a hypothesis test. This means that there is a very low probability of obtaining the observed results by chance alone, and supports the rejection of the null hypothesis.

  6. 3 days ago · The CHITEST function in Excel is a statistical tool that allows users to determine the significance of differences between two sets of data. It calculates the p-value, which is a measure of the probability that the observed differences between the two sets of data occurred by chance.

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  8. 3 days ago · The accompanying calculator display shows results from a test of the claim that less than 11 % of treated subjects experienced headaches. Use the normal distribution as an approximation to the binomial distribution and assume a 0.01 significance level to complete parts (a) through (e) below.

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