You can't do this without knowing the distribution of scores.
-1.28
When putting the scores in, you use the normal distribution graph, which is the best start.
The median of a distribution of scores is the middle value when the scores are arranged in ascending or descending order. If there is an odd number of scores, the median is the middle score; if there is an even number, it is the average of the two middle scores. The median is a measure of central tendency that is less affected by outliers than the mean, making it a useful indicator of the typical score in a dataset.
To find the proportion of a normal distribution corresponding to z-scores greater than +1.04, you can use the standard normal distribution table or a calculator. The area to the left of z = 1.04 is approximately 0.8508. Therefore, the proportion of the distribution that corresponds to z-scores greater than +1.04 is 1 - 0.8508, which is approximately 0.1492, or 14.92%.
You call it a bell shaped curved. It may or may not be Gaussian (Normal).
The mean of a distribution of scores is the average.
The pattern is called a normal distribution, or a bell curve. It is characterized by symmetrical data points around the mean, with most scores clustering around the average and progressively fewer scores as you move away from the mean in either direction.
You can't do this without knowing the distribution of scores.
They are said to be Normally distributed.
The distribution is skewed to the right.
If the distribution is Gaussian (or Normal) use z-scores. If it is Student's t, then use t-scores.
This simply means that if you plot a histogram of the scores it will be asymmetric.
If most the population has many high scores, the distribution is negatively skewed. If most have many low scores, it is positively skewed
z-scores are distributed according to the standard normal distribution. That is, with the parameters: mean 0 and variance 1.
The IQs of a large enough population can be modeled with a Normal Distribution
Simple frequency distribution is a method of organizing large data sets into more easily interpreted sets. An example is organizing sample test scores by the individual scores.