**Core Concept**
The standard error (SE) of a sample mean is a measure of the variability of the sample mean compared to the true population mean. It is calculated by dividing the standard deviation (SD) of the sample by the square root of the sample size.

**Why the Correct Answer is Right**
The correct formula for calculating the standard error is SE = SD / √n, where SD is the standard deviation of the sample and n is the sample size. In this case, the standard deviation is 2 gm% and the sample size is 100. Plugging these values into the formula, we get SE = 2 / √100 = 0.2.

Option A is incorrect because it does not take into account the sample size. Option B is incorrect because it incorrectly calculates the standard error by dividing the sample size by the standard deviation. Option C is incorrect because it is a random value and does not follow the correct formula.

**Why Each Wrong Option is Incorrect**
**Option A:** Incorrect because it does not consider the sample size in the calculation.
**Option B:** Incorrect because it reverses the formula for calculating the standard error.
**Option C:** Incorrect because it is a random value and does not follow the correct formula.

**Clinical Pearl / High-Yield Fact**
When calculating the standard error, remember to divide the standard deviation by the square root of the sample size, not the sample size itself.

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