**Core Concept:** Random sampling is a statistical method used to obtain a representative sample from a larger population. In this context, the chance of an individual being picked up in a random sample depends on the size of the population being sampled.

**Why the Correct Answer is Right:**
In probability theory, the size of the population is directly proportional to the number of possible outcomes. For simplicity, let's consider a binary situation: either an individual is selected or not. In a population of size N, the number of possible outcomes is 2^N (2 raised to the power of N).

For example, if N = 5 (representing 5 individuals), the number of possible outcomes would be 2^5 = 32 (1, 2, 4, 8, 16, 32).

When N increases, the number of possible outcomes also increases exponentially, making the probability of selecting any individual in a random sample lower.

**Why Each Wrong Option is Incorrect:**
A. This option is incorrect because the probability is directly proportional to the size of the population, as mentioned above.
B. This option is incorrect because the probability is directly proportional to the size of the population.
C. This option is incorrect for the same reason as Option B.
D. This option is incorrect for the same reason as Option B and C.

**Clinical Pearl:** To maintain a reasonable probability of selecting a person in a random sample, ensure that the population size is known and appropriately considered when selecting the sample size.

**Correct Answer:** D. The correct answer is "1/N," which represents the probability of selecting a person in a random sample from a population of size N. As the population size increases, the probability decreases. This concept is crucial in medical research, epidemiological studies, and clinical trials, where random sampling is often employed to ensure unbiased results.