What is the purpose of inferential statistics?
Explanation: The correct answer is C) To draw conclusions about a population based on sample data. Inferential statistics is used to make inferences or generalizations about a larger population based on a sample of data. It involves using statistical techniques to estimate population parameters and test hypotheses.
What is a null hypothesis in inferential statistics?
Explanation: The correct answer is A) A hypothesis that states there is no relationship between variables. In inferential statistics, the null hypothesis is a statement that assumes there is no significant relationship or difference between variables being studied. It is often contrasted with the alternative hypothesis, which suggests the presence of a relationship or difference.
What does a p-value represent in inferential statistics?
Explanation: The correct answer is B) The probability of observing the data given that the null hypothesis is true. In inferential statistics, the p-value represents the probability of obtaining the observed data, or more extreme data, assuming that the null hypothesis is true. It is used to determine the level of evidence against the null hypothesis and make decisions about its rejection or acceptance.
What is the significance level commonly used in hypothesis testing?
Explanation: The correct answer is A) 0.05. The significance level, often denoted as alpha (α), is the probability threshold used in hypothesis testing to determine whether the observed results are statistically significant. The commonly used significance level is 0.05, corresponding to a 5% chance of obtaining the observed results under the null hypothesis by random chance.
What is the purpose of confidence intervals in inferential statistics?
Explanation: The correct answer is B) To provide a range of plausible values for the population parameters. Confidence intervals in inferential statistics are used to estimate the range of plausible values for population parameters based on sample data. They provide a measure of the precision and uncertainty associated with the estimates and help in making inferences about the population.