Which statement best describes reliability and validity in data?

Which statement best describes reliability and validity in data?

• A. Reliability = consistency; validity = whether the data measure what they intend to measure.
• B. Reliability = accuracy; validity = bias present.
• C. Reliability = sample size; validity = data type.
• D. Reliability = what data collects; validity = dataset length.

Answer: (A)

Reliability and validity describe how good our measurements are. Reliability means the measurements are consistent—you'd expect the same result if you repeat the measurement under the same conditions. Validity means the measurement actually measures what you intend to measure, i.e., it accurately reflects the concept you’re studying. The statement that reliability equals consistency and validity equals whether the data measure what they intend to measure captures this idea precisely. It distinguishes consistency from correctness: you want data that are both reliable (stable across repeats) and valid (true to the concept). It also highlights that reliability and validity are not the same thing. Other descriptions mix up terms—for example, reliability is not the same as accuracy, and validity isn’t simply about bias or about factors like sample size, data type, or dataset length. In practice, you can have reliable data that aren’t valid if they consistently measure the wrong thing, or valid data that aren’t reliable if the measurements vary a lot.

Reliability and validity describe how good our measurements are. Reliability means the measurements are consistent—you'd expect the same result if you repeat the measurement under the same conditions. Validity means the measurement actually measures what you intend to measure, i.e., it accurately reflects the concept you’re studying.

The statement that reliability equals consistency and validity equals whether the data measure what they intend to measure captures this idea precisely. It distinguishes consistency from correctness: you want data that are both reliable (stable across repeats) and valid (true to the concept). It also highlights that reliability and validity are not the same thing.

Other descriptions mix up terms—for example, reliability is not the same as accuracy, and validity isn’t simply about bias or about factors like sample size, data type, or dataset length. In practice, you can have reliable data that aren’t valid if they consistently measure the wrong thing, or valid data that aren’t reliable if the measurements vary a lot.