what has been done in the past. Changes from the status quo will typically involve both gains and losses, with the change having good overall consequences if the gains outweigh these losses. A tendency to overemphasize the avoidance of losses will thus favour retaining the status quo, resulting in a status quo bias. Even though choosing the status quo may entail forfeiting certain positive consequences, when these are represented as forfeited "gains" they are psychologically given less weight than the "losses" that would be incurred if the status quo were changed. The loss aversion explanation for the status quo bias has been challenged by David Gal and Derek Rucker who argue that evidence for loss aversion (i.e., a tendency to avoid losses more than to pursue gains) is confounded with a tendency towards inertia (a tendency to avoid intervention more than to intervene in the course of affairs). Inertia, in this sense, is related to omission bias, except it need not be a bias but might be perfectly rational behavior stemming from transaction costs or lack of incentive to intervene due to fuzzy preferences. Omission bias Omission bias may account for some of the findings previously ascribed to status quo bias. Omission bias is diagnosed when a decision maker prefers a harmful outcome that results from an omission to a less harmful outcome that results from an action. Overall implications of a study conducted by Ilana Ritov and Jonathan Baron, regarding status quo and omission biases, reveal that omission bias may further be diagnosed when the decision maker is unwilling to take preference from any of the available options given to them, thus enabling reduction of the number of decisions where utility comparison and weight is unavoidable. Detection The reversal test: when a proposal to change a certain parameter is thought to have bad overall consequences, consider a change to the same parameter in the opposite direction. If this is also thought to have bad overall consequences, then the onus is on those who reach these conclusions to explain why our position cannot be improved through changes to this parameter. If they are unable to do so, then we have reason to suspect that they suffer from status quo bias. The rationale of the reversal test is: if a continuous parameter admits of a wide range of possible values, only a tiny subset of which can be local optima, then