Which statement accurately describes descriptive statistics and inferential statistics?

Which statement accurately describes descriptive statistics and inferential statistics?

• A. Descriptive statistics help determine if results are due to chance; inferential statistics summarize groups.
• B. Descriptive statistics compare means; inferential statistics summarize distributions.
• C. Descriptive tells you about the groups (mean, median, mode, age, gender) while inferential helps you draw conclusions as to whether the outcome is due to the intervention or due to chance.
• D. Descriptive statistics are used for hypothesis testing; inferential statistics are used for data collection.

Answer: (C)

Descriptive statistics describe what the data look like for the sample: they summarize central tendency and spread (like the mean, median, and mode) and note characteristics such as age or gender, as well as how the data are distributed. Inferential statistics use that information to make inferences about a larger population or to test whether observed effects are likely due to chance, often through hypothesis tests and estimates like confidence intervals. The statement that descriptive statistics tell you about the groups (mean, median, mode, age, gender) while inferential statistics help you determine whether the outcome is due to the intervention or to chance captures this distinction well. It reflects the practical use: describe who your sample is and what it looks like, then assess whether the results generalize beyond the sample or reflect a real effect rather than random variation. Other options mix up these roles—for example, treating descriptive statistics as determining if results are due to chance, or suggesting descriptive statistics focus on comparing means or that inferential statistics summarize distributions—so those descriptions don’t align with how these tools are actually used.

Descriptive statistics describe what the data look like for the sample: they summarize central tendency and spread (like the mean, median, and mode) and note characteristics such as age or gender, as well as how the data are distributed. Inferential statistics use that information to make inferences about a larger population or to test whether observed effects are likely due to chance, often through hypothesis tests and estimates like confidence intervals. The statement that descriptive statistics tell you about the groups (mean, median, mode, age, gender) while inferential statistics help you determine whether the outcome is due to the intervention or to chance captures this distinction well. It reflects the practical use: describe who your sample is and what it looks like, then assess whether the results generalize beyond the sample or reflect a real effect rather than random variation. Other options mix up these roles—for example, treating descriptive statistics as determining if results are due to chance, or suggesting descriptive statistics focus on comparing means or that inferential statistics summarize distributions—so those descriptions don’t align with how these tools are actually used.