Statistical Inference

What is a confidence interval in hypothesis testing?

• A confidence interval is a measure of how certain the hypothesis test results are.

• A confidence interval is a range of values that is likely to contain the sample mean.

• A confidence interval in hypothesis testing is a range of values that is likely to contain the true population parameter.

• A confidence interval is a specific value that represents the population parameter.

Define the level of significance in statistical analysis.

• The level of significance is the margin of error in a statistical analysis.

• The level of significance is the confidence interval in a statistical analysis.

• The level of significance in statistical analysis is the probability of rejecting the null hypothesis when it is actually true.

• The level of significance is the sample size in a statistical analysis.

Explain the concept of p-value and its significance in hypothesis testing.

• A high p-value (typically ≥ 0.05) indicates strong evidence against the null hypothesis.

• The p-value is a measure of the strength of the evidence against the null hypothesis. A low p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, leading to its rejection.

• The p-value is always fixed and does not change based on the data.

• The p-value is a measure of the strength of the evidence in favor of the null hypothesis.

What does NPC stand for in statistical analysis?

• Non-Parametric Calculus

• National Population Census

• Numerical Probability Calculation

• Non-Parametric Combination

How is skewness measured in a dataset?

• Skewness = 2 * (Mean - Median) / Standard Deviation

• Skewness = (Mean - Median) / Standard Deviation

• Skewness = (Mean - Median) * Standard Deviation

• Skewness = 3 * (Mean - Median) / Standard Deviation

What is the null hypothesis in hypothesis testing?

• A statement that there is no need to conduct hypothesis testing.

• A statement that there is a significant difference between specified populations or variables.

• A statement that there is a moderate difference between specified populations or variables.

• A statement that there is no significant difference or relationship between specified populations or variables.

What is the alternative hypothesis in hypothesis testing?

• The alternative hypothesis is the hypothesis that is never tested.

• The alternative hypothesis is the hypothesis that is considered when the null hypothesis is unlikely to be true.

• The alternative hypothesis is the hypothesis that is irrelevant to the study.

• The alternative hypothesis is the hypothesis that is always true.

How is a research question formulated in statistical analysis?

• Start the analysis without a specific question

• Use only qualitative data for formulating the question

• Include irrelevant variables in the question

• Identify the problem or topic of interest, determine the population or sample, specify the variables to measure, and decide on the type of analysis.

What is a Type I error in hypothesis testing?

• Rejecting a false null hypothesis

• Failing to reject a true null hypothesis

• Rejecting a true null hypothesis

• Accepting a false null hypothesis

Define Type II error in statistical analysis.

• Type II error is the error of rejecting a true null hypothesis.

• Type II error is the error of accepting a false null hypothesis.

• Type II error is the error of failing to reject a true null hypothesis.

• Type II error is the error of failing to reject a false null hypothesis.