To ensure fairness, once a valid application has been submitted for assessment, a minimum of five reviewers will grade each submission. Those reviewers will offer both scores and comments against each of four distinct traits. Each trait will be scored on a 0-5 point scale, in increments of 0.1. Those scores will combine to produce a total normalized score. Examples of possible scores for a trait are… 1.4, 3.7, *etc*.

The most straightforward way to ensure that everyone is treated by the same set of standards would be to have the same reviewers score every application; unfortunately, due to the number of applications that we may receive, that is not possible.

Since the same reviewers will not score every application, the question of fairness needs to be explained carefully. One reviewer scoring an application may take a more critical view, giving any assigned team a range of scores only between 1.0 and 2.0, as an example; meanwhile, another reviewer may be more generous and want to score every submission between 4.0 and 5.0.

For illustrative purposes, let’s look at the scores from two hypothetical reviewers:

The first reviewer is far more generous, as a scorer, than the second reviewer, who gives much lower scores. If your application was rated by the first reviewer, it would earn a much higher total score than if it was assigned to the second reviewer.

We have a way to address this issue. We ensure that no matter which reviewers are assigned to your team, each application will be treated fairly. To do this, we utilize a mathematical technique relying on two measures of distribution, the *mean* and the *standard deviation*.

The mean takes all the scores assigned by a reviewer, adds them up, and divides them by the number of scores assigned, giving an average score.

Formally, we denote the mean like this:

The standard deviation measures the “spread” of a reviewer’s scores. As an example, imagine that two reviewers both give the same mean (average) score, but one gives many zeros and fives, while the other gives more ones and fours. It wouldn't be fair, if we didn’t consider this difference.

Formally, we denote the standard deviation like this:

To ensure that the judging process is fair, we rescale all the scores to match the judging population. In order to do this, we measure the mean and the standard deviation of all scores across all judges. Then, we change the mean score and the standard deviation of each judge to match.

We rescale the standard deviation like this:

Then, we rescale mean like this:

Basically, we are finding the difference between both distributions for a single reviewer and those for all of the reviewers combined, then adjusting each score so that no one is treated unfairly according to which reviewers they are assigned.

If we apply this rescaling process to the same two reviewers in the example above, we can see the outcome of the final resolved and normalized scores. They appear more similar, because they are now aligned with typical distributions across the total population of reviewers.

We are pleased to answer any questions you have about how we seek to ensure a level playing field for every team. Please register today and join our discussion forums, where you can ask questions or seek clarification.