Descriptive and inferential statistics work hand in hand, and which one you use and when depends on:
a. The question you want answered.
b. The methods you choose for investigation.
c. The sample you select.
d. The population you choose.
The correct answer and explanation is:
The correct answer is: a. The question you want answered.
Descriptive and inferential statistics are complementary methods used in data analysis, but they serve different purposes. Descriptive statistics summarize and organize data, providing insights into patterns and trends within the sample. They include measures like mean, median, mode, and standard deviation. These statistics help you describe the data you have without making generalizations beyond the dataset.
On the other hand, inferential statistics are used to make predictions or inferences about a population based on a sample. Through methods like hypothesis testing, confidence intervals, and regression analysis, inferential statistics allow you to draw conclusions that extend beyond the immediate data set, estimating characteristics of a larger population.
The decision to use one over the other depends primarily on the question you want answered. If you are interested in summarizing or presenting the data you’ve already collected, descriptive statistics are most appropriate. For example, if you want to know the average test score of a class, descriptive statistics can provide that information directly. However, if your goal is to make predictions or generalize results to a broader population, then you would use inferential statistics. For example, if you’re conducting a survey and want to predict the behavior of a larger group based on a sample, inferential statistics would be necessary.
While the sample, population, and methods are important factors, the type of question you are asking about the data will primarily determine whether descriptive or inferential statistics are needed.