**Core Concept**
Type-II error, also known as a **false negative**, occurs when a test fails to detect a statistically significant difference or effect when one actually exists. This type of error is particularly concerning in clinical settings where it can lead to delayed or missed diagnoses.

**Why the Correct Answer is Right**
Type-II error occurs due to the **lack of statistical power** in a study, which is often caused by a small sample size or inadequate sensitivity of the diagnostic test. As a result, the test fails to detect an effect or difference that is truly present, leading to a false negative result. This can have serious consequences in clinical practice, such as delaying treatment or misdiagnosing a patient.

**Why Each Wrong Option is Incorrect**
* **Option A:** This option is incorrect because type-I error, not type-II error, is related to the **false positive** rate. Type-I error occurs when a test detects a statistically significant difference or effect when one does not exist.
* **Option B:** This option is incorrect because specificity, not type-II error, refers to the ability of a test to correctly identify true negatives. While specificity is an important characteristic of a diagnostic test, it is not directly related to type-II error.
* **Option C:** This option is incorrect because the **confidence interval** is a statistical tool used to estimate the range of values within which a population parameter is likely to lie. While confidence intervals can be used to estimate the magnitude of an effect or difference, they are not directly related to type-II error.

**Clinical Pearl / High-Yield Fact**
To minimize the risk of type-II error, clinicians should strive to use diagnostic tests with high sensitivity and adequate sample sizes to detect statistically significant differences or effects.

**Correct Answer: B. False negative**