**Core Concept**
The standard error (SE) of a sample mean is a measure of the variability or uncertainty associated with the sample mean as an estimate of the population mean. It is calculated by dividing the standard deviation (SD) of the sample by the square root of the sample size (n).

**Why the Correct Answer is Right**
To calculate the standard error, we use the formula: SE = SD / √n. In this case, the standard deviation (SD) of the hemoglobin levels is 2 gm%, and the sample size (n) is 100 women. Plugging these values into the formula, we get: SE = 2 / √100 = 2 / 10 = 0.2. This means that the standard error of the sample mean is 0.2 gm%.

**Why Each Wrong Option is Incorrect**
**Option A:** This option is incorrect because it does not take into account the sample size (n). The correct formula for standard error is SE = SD / √n, not just SE = SD.

**Option B:** This option is incorrect because it is the standard deviation (SD) itself, not the standard error (SE). The standard error is a measure of the variability of the sample mean, not the sample data.

**Option C:** This option is incorrect because it uses the population size (N) instead of the sample size (n). The correct formula for standard error is SE = SD / √n, not SE = SD / √N.

**Option D:** This option is incorrect because it is a made-up value and does not follow the correct formula for standard error.

**Clinical Pearl / High-Yield Fact**
Remember that the standard error is a measure of the uncertainty associated with a sample mean, and it decreases as the sample size increases. This is why larger samples typically provide more precise estimates of the population mean.

**Correct Answer:** **Correct Answer:** A. 0.2