Q: What is hypothesis testing? A: Hypothesis testing is a statistical method used to make decisions about a population parameter based on a sample of data. The process involves formulating a hypothesis about the population, collecting sample data, and using statistical tests to determine whether the data supports or contradicts the hypothesis.
Q: What are the two types of hypotheses in hypothesis testing? A: In hypothesis testing, there are two types of hypotheses: the null hypothesis and the alternative hypothesis. The null hypothesis is a statement that assumes there is no significant difference between two populations, while the alternative hypothesis is a statement that assumes there is a significant difference between two populations.
Q: What is a Type I error in hypothesis testing? A: A Type I error occurs when a researcher rejects a null hypothesis that is actually true. This is also known as a false positive.
Q: What is a Type II error in hypothesis testing? A: A Type II error occurs when a researcher fails to reject a null hypothesis that is actually false. This is also known as a false negative.
Q: What is the p-value in hypothesis testing? A: The p-value is the probability of obtaining a test statistic as extreme as or more extreme than the one observed, assuming that the null hypothesis is true. It is used to determine whether the null hypothesis should be rejected or not.
Q: What is the significance level in hypothesis testing? A: The significance level is the probability of rejecting the null hypothesis when it is actually true. It is typically set at 0.05 or 0.01, and is denoted by the Greek letter alpha (α).
Q: How do you interpret a p-value in hypothesis testing? A: If the p-value is less than the significance level (i.e., p < α), then the null hypothesis is rejected in favor of the alternative hypothesis. If the p-value is greater than or equal to the significance level (i.e., p ≥ α), then the null hypothesis is not rejected.
Q: What is a one-tailed test in hypothesis testing? A: A one-tailed test is a hypothesis test in which the alternative hypothesis is either greater than or less than the null hypothesis, but not both.
Q: What is a two-tailed test in hypothesis testing? A: A two-tailed test is a hypothesis test in which the alternative hypothesis is that the parameter is not equal to the null value, and can be either greater than or less than the null value.
Q: What is the difference between a confidence interval and a hypothesis test? A: A confidence interval is a range of values within which the true population parameter is likely to fall with a certain degree of confidence. A hypothesis test, on the other hand, is used to determine whether there is sufficient evidence to reject or fail to reject a null hypothesis about a population parameter.