The formula for calculating a Z-score is given below (see Fig. 2): As the formula shows, the z-score is simply the raw score minus the sample mean, divided by the sample standard deviation.

Figure 2. Z-score formula.

Sometimes we know a Z-score and want to find the corresponding raw score. The formula for calculating a Z-score in a sample into a raw score is given below:

X = (z)(SD) + mean

As the formula shows, the Z-score and standard deviation are multiplied together, and this figure is added to the mean.

Check your answer makes sense: If we have a negative Z-score the corresponding raw score should be less than the mean, and a positive Z-score must correspond to a raw score higher than the mean.

To calculate the Z-score of a specific value, x, first you must calculate the mean of the sample by using the AVERAGE formula.

For example, if the range of scores in your sample begin at cell A1 and end at cell A20, the formula =AVERAGE(A1:A20) returns the average of those numbers.

Next, you mush calculate the standard deviation of the sample by using the STDEV.S formula. For example, if the range of scores in your sample begin at cell A1 and end at cell A20, the formula = STDEV.S (A1:A20) returns the standard deviation of those numbers.

Now to calculate the Z-score type the following formula in an empty cell: = (x – mean) / [standard deviation].

To make things easier, instead of writing the mean and SD values in the formula you could use the cell values corresponding to these values. For example, = (A12 – B1) / [C1].

Then, to calculate the probability for a SMALLER Z-score, which is the probability of observing a value less than x (the area under the curve to the LEFT of x), type the following into a blank cell: = NORMSDIST (and input the Z-score you calculated).

To find the probability of LARGER Z-score, which is the probability of observing a value greater than x (the area under the curve to the RIGHT of x), type: =1 – NORMSDIST (and input the Z-score you calculated).

The value of the Z-score tells you how many standard deviations you are away from the mean. If a Z-score is equal to 0, it is on the mean.

A positive Z-score indicates the raw score is higher than the mean average. For example, if a Z-score is equal to +1, it is 1 standard deviation above the mean.

A negative Z-score reveals the raw score is below the mean average. For example, if a Z-score is equal to -2, it is 2 standard deviations below the mean.

Another way to interpret Z-scores is by creating a standard normal distribution (also known as the Z-score distribution or probability distribution).

Fig 3 illustrates the important features of any standard normal distribution (SND).

Figure 3. A standard normal distribution (SND) and normal distribution.

1. The SND (i.e. z-distribution) is always the same shape as the raw score distribution. For example, if the distribution of raw scores if normally distributed, so is the distribution of Z-scores.
2. The mean of any SND always = 0.
3. The standard deviation of any SND always = 1. Therefore, one standard deviation of the raw score (whatever raw value this is) converts into 1 Z-score unit.

The SND allows researchers to calculate the probability of randomly obtaining a score from the distribution (i.e. sample). For example, there is a 68% probability of randomly selecting a score between -1 and +1 standard deviations from the mean (see Fig. 4).

Figure 4. Proportion of a standard normal distribution (SND) in percentages.

The probability of randomly selecting a score between -1.96 and +1.96 standard deviations from the mean is 95% (see Fig. 4). If there is less than a 5% chance of a raw score being selected randomly, then this is a statistically significant result.

In statistics, a mean is defined as the simple average of the given set of values or quantities. The median is said to be the middle number in an ordered list of values. While mean is the arithmetic average, the median is positional average, in essence, the position of the data set determines the value of median.

If a frequency distribution graph has a symmetrical frequency curve, then mean, median and mode will be equal. In case of a positively skewed frequency distribution, the mean is always greater than median and the median is always greater than the mode.

What can the median tell us? The median provides a helpful measure of the center of a dataset. By comparing the median to the mean, we can get an idea of the distribution of a dataset. When the mean and the median are the same, the dataset is more or less evenly distributed from the lowest to highest values.

A Youden Plot is a type of scatter plot used primarily in plotting results from multiple laboratories on the same graph. It is meant to uncover bias in measurement. Youden Plots are used in medical analysis and quality control, and allow you to see quickly and easily:

• If labs are equivalent,
• Which particular labs are outliers,
• Whether inconsistencies are primarily between labs (reproducibility problems) or within a lab (repeatability problems).
• The Youden Plot and the analysis it involves, is also called a two-sample diagram, two-sample plan, or two-sample collaborative testing.