Inferential statistics

What is the primary purpose of inferential statistics?

• To calculate the mean of a sample.
• To summarize data using graphs and charts.
• To determine the exact values of a population.
• To make conclusions about a population based on sample data.

Which of the following best describes the difference between a population and a sample?

• A population is a small group, and a sample is the entire group.
• A population is a random selection, and a sample is a fixed number of individuals.
• A population is always larger than a sample, regardless of context.
• A population is the whole group of interest, while a sample is a smaller group chosen from that population.

What is a hypothesis in the context of inferential statistics?

• A statement we want to test

• A definitive conclusion drawn from data.

• An untestable assumption about the variables.

• A summary of past research findings.

What does hypothesis testing involve?

• testing a claim about a population parameter using sample data

• testing requires prior knowledge of the entire population.

• testing is a method for collecting data from multiple sources.

• testing is only applicable to large datasets.

What is the first step in hypothesis testing?

• 1. Drawing conclusions from the data

• 1. Calculating the P value

• 1. Assuming the null hypothesis is true before testing

• 1. Collecting data from the sample

. What implication does rejecting the null hypothesis have?

• It suggests there is evidence against the null hypothesis.
• It shows the sample is perfectly representative.
• It guarantees the results are significant.
• It proves the null hypothesis is false.

. What implication does rejecting the null hypothesis have?

• It suggests there is evidence against the null hypothesis.
• It shows the sample is perfectly representative.
• It guarantees the results are significant.
• It proves the null hypothesis is false.

What does the P value indicate in hypothesis testing?

• The confidence level of the test
• The probability of obtaining a sample that deviates from the null hypothesis
• The effect size of the treatment
• The duration of the study

When is a result considered statistically significant?

• When the results are visually appealing
• When the P value is less than a predetermined threshold, often 0.05
• When the sample size is irrelevant
• When the P value is exactly 0.05

What does a small P value suggest about the null hypothesis?

• The observed data is inconsistent with the null hypothesis, leading us to reject it.
• A small P value suggests the data is random and unreliable.
• The null hypothesis is always true regardless of the data.
• A small P value indicates a strong correlation.