Step 1: Recall the definition of the median.
The median is the middle value (or average of two middle values) when all scores are arranged in order.
By definition, at least half the candidates have scores less than or equal to the median, and at least half have scores greater than or equal to the median.
Step 2: Recall the definition of the mean.
The mean (average) can be influenced heavily by extreme values (outliers). Hence, no fixed relation (greater or smaller) must always hold between the mean and the median.
Step 3: Eliminate incorrect options.
- (A) Median ≥ Mean → Not always true. In skewed distributions, mean can be larger.
- (B) Mean ≥ Median → Not always true either; depends on skewness.
- (D) At most half the candidates have a score larger than the mean → Not necessarily true. For example, if many students score below the mean, more than half could be above the mean.
Step 4: Verify correct option.
(C) "At most half the candidates have a score that is larger than the median" → This is always true because by definition, the median divides the data into two equal halves.
Final Answer: \[ \boxed{\text{At most half the candidates have a score larger than the median.}} \]





While doing Bayesian inference, consider estimating the posterior distribution of the model parameter (m), given data (d). Assume that Prior and Likelihood are proportional to Gaussian functions given by \[ {Prior} \propto \exp(-0.5(m - 1)^2) \] \[ {Likelihood} \propto \exp(-0.5(m - 3)^2) \] 
The mean of the posterior distribution is (Answer in integer)
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Suppose a mountain at location A is in isostatic equilibrium with a column at location B, which is at sea-level, as shown in the figure. The height of the mountain is 4 km and the thickness of the crust at B is 1 km. Given that the densities of crust and mantle are 2700 kg/m\(^3\) and 3300 kg/m\(^3\), respectively, the thickness of the mountain root (r1) is km. (Answer in integer)