For any distribution (normal or not normal), the probability of selecting a score greater than the median is

Median is always the 50th percentile.

Well, selecting a score greater than the median is like picking a winning lottery ticket, except the stakes aren't as high. The probability depends on the shape of the distribution and how the scores are spread out. In a normal distribution, the probability is 0.5. It's like a coin flip, but instead of heads or tails, it's "greater than the median" or "less than the median". However, in a not normal distribution, things get a bit trickier. You might need to consult a magician or a statistician to help you out with the specific probabilities, because it can vary depending on the shape of the distribution. But hey, if you ever find yourself struggling with the probability of selecting a score greater than the median, just remember this: laughter is always the best distribution!

The median is the middle value of a distribution, separating the data into two equal halves. Therefore, the probability of selecting a score greater than the median would depend on the specific distribution.

For a normal distribution, which is a symmetric bell-shaped curve, half of the scores are greater than the median, and half are lower. Thus, the probability of selecting a score greater than the median is 0.5 or 50%.

For a non-normal distribution, the probability of selecting a score greater than the median would still depend on the specific shape of the distribution. If the distribution is skewed to the right, where the tail is longer on the right side, then the probability of selecting a score greater than the median would be less than 50%. Conversely, if the distribution is skewed to the left, where the tail is longer on the left side, the probability of selecting a score greater than the median would be greater than 50%.

In summary, the probability of selecting a score greater than the median will vary depending on the distribution.

To find the probability of selecting a score greater than the median for any distribution, you would need to know the specific characteristics of the distribution, such as its shape, parameters, or the data values.

If you have the probability density function (PDF) or cumulative distribution function (CDF) of the distribution, you can use those to estimate the probability. Here's a general approach:

1. Start by finding the median of the distribution. For a normal distribution, the median is equal to the mean. For other distributions, you may need to use different methods, such as interpolation or solving equations, depending on the distribution.

2. Once you have the median, you can use the CDF to find the probability of selecting a score greater than the median. The CDF gives the probability that a random variable is less than or equal to a specific value. To find the probability of selecting a score greater than the median, you can subtract the CDF value at the median from 1.

Alternatively, if you have a large sample from the distribution, you can estimate the probability empirically. You can count the number of scores greater than the median in the sample and divide it by the total number of scores in the sample.

In summary, the probability of selecting a score greater than the median for any distribution can be determined by using the CDF or by empirical estimation if you have a sample from the distribution.