Everyone is treated fairly.

Once a candidate has completed an application, a minimum of five peers will be assigned to score each proposal. Peers will offer both scores and comments for each of four distinct scoring criteria. Each of the four traits will be scored on a 0-5 point scale, in increments of 0.1. Those scores will combine to produce a total score. Examples of possible scores for a trait are… 0.4, 3.7, 5.0, *etc*.

The most straightforward way to ensure that every candidate is treated by the same set of standards would be to have the same individuals score every other application; unfortunately, due to the number of candidates that we will receive, that is not possible.

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

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

The first peer is far more generous in scoring than the second one, who gives much lower scores. If a candidate’s application was rated by the first peer, it would earn a much higher total score than if it was assigned to the second peer.

We have a way to address this issue. We ensure that no matter which peers are assigned, 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 peer, 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 peer’s scores. As an example, imagine that two peers 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 peer review process is fair, we rescale all the scores to match the peer population. In order to do this, we measure the mean and the standard deviation of all scores across all peers. Then, we change the mean score and the standard deviation of each peer 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 peer and those for all of the peers combined, then adjusting each score so that no one is treated unfairly according to which peers they are assigned.

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

We are pleased to answer any questions you have about the scoring process. Please feel free to ask any questions on the discussion board. Please register today to begin developing your application.