Which of the following statements best describes the relationship between $\mathrm{validity}$ and $\mathrm{reliability}$ in the context of probability and statistics?

• A. $\mathrm{Reliability}$ refers to accuracy, while $\mathrm{validity}$ refers to consistency.
• B. $\mathrm{Reliability}$ and $\mathrm{validity}$ are unrelated concepts; data can be $\mathrm{valid}$ without being $\mathrm{reliable}$.
• C. Data can be $\mathrm{reliable}$ without being $\mathrm{valid}$, but $\mathrm{valid}$ data must always be $\mathrm{reliable}$.
• D. All $\mathrm{reliable}$ data is automatically $\mathrm{valid}$.

Answer: (C)

Verified step by step guidance

1. Step 1: Understand the definitions of reliability and validity in statistics. Reliability refers to the consistency or repeatability of measurements, meaning if you measure the same thing multiple times under the same conditions, you get similar results each time.
2. Step 2: Recognize that validity refers to the accuracy or truthfulness of the measurement — whether the measurement actually measures what it is intended to measure.
3. Step 3: Analyze the relationship: Data can be reliable without being valid if measurements are consistent but systematically incorrect (e.g., a faulty scale that always reads 5 pounds too heavy).
4. Step 4: Understand that for data to be valid, it must also be reliable because if measurements are not consistent, they cannot accurately represent the true value.
5. Step 5: Conclude that the best description is: 'Data can be reliable without being valid, but valid data must always be reliable,' reflecting that reliability is necessary but not sufficient for validity.