Hypothesis Testing Cheat Sheet - Qlmacros

The "Hypothesis Testing Cheat Sheet - Qlmacros" is a document designed to provide quick reference and guidance on hypothesis testing. It is useful for understanding and applying statistical tests to analyze data and make inferences about a population.

FAQ

Q: What is hypothesis testing?
A: Hypothesis testing is a statistical method used to make inferences about population parameters based on sample data.

Q: What is a null hypothesis?
A: The null hypothesis is a statement of no effect or no difference in the population parameters.

Q: What is an alternative hypothesis?
A: The alternative hypothesis is a statement that contradicts the null hypothesis and suggests there is a difference or effect in the population parameters.

Q: What is a p-value?
A: The p-value is a probability value that measures the strength of evidence against the null hypothesis.

Q: How do you interpret a p-value?
A: A smaller p-value indicates stronger evidence against the null hypothesis, suggesting that the observed results are unlikely to occur by chance.

Q: What is a significance level?
A: The significance level, often denoted as alpha (α), is the threshold at which the p-value is considered statistically significant.

Q: What is a type I error?
A: A type I error, also known as a false positive, occurs when the null hypothesis is rejected when it is actually true.

Q: What is a type II error?
A: A type II error, also known as a false negative, occurs when the null hypothesis is not rejected when it is actually false.

Q: What is a one-sample t-test?
A: A one-sample t-test is used to compare the mean of a single sample to a known population mean.

Q: What is a two-sample t-test?
A: A two-sample t-test is used to compare the means of two independent samples to determine if they are significantly different.

Q: What is a chi-square test?
A: A chi-square test is used to determine if there is a significant association between two categorical variables.