Sensitivity analysis is post primary analysis which tells us how robust our results are. It can give specific information on which assumptions are critical and in what degree they affect research results, as well as what extent changes in methods, models, and the values of unmeasured variables affect the results.

Sensitivity analysis is also known as “what-if” analysis, as it focuses on what happens to the dependent variable when various parameters change. It is important in all fields of scientific and statistical research.

## Purposes of Sensitivity Analysis

Sensitivity Analysis can help us to identify crucial connections between model inputs, predictions and forecasts, and observations. It gives us a way to identify sensitive parmaters, as well as determine which parameters are non-sensitive. This means that it may help us simplify our models, by eliminating both input variables which have no actual affect on the data as well as redundant structures.

Unexpected relationships between parameters and results can point to errors in our model. Sensitivity analysis can also help us redesign our experiments; as we find which parameters are most sensitive, we can design the experiment to decrease uncertainty in that parameter.

## One-at-a-time (OAT or OFAT) Analysis

One-at-a-time analysis (also known a one-factor-at-a-time, or OFAT, analysis)is one of the simplest and most rewarding ways of running system analysis. All but one variable are kept at baseline variables, and that single variable is varied while new readings are taking. Then the test variable is returned to its baseline value, and another reading taken. Each parameter is tested in this way one at a time.

The weak point of this method is that, in focusing on each variable individually, it doesn’t make allowance for interaction between variables or allow us to pinpoint joint effects; effects which might happen when several variables change simultaneously.

## References

Pannel, David. Sensitivity analysis: strategies, methods, concepts, examples. Modified from Pannell, D.J. (1997). Sensitivity analysis of normative economic models: Theoretical framework and practical strategies, Agricultural Economics 16: 139-152. Retrieved from http://dpannell.fnas.uwa.edu.au/dpap971f.htm on July 5, 2018

Financial Modeling Techniques: Sensitivity Analysis (“What if” Analysis)

Retrieved from https://www.wallstreetprep.com/knowledge/financial-modeling-techniques-sensitivity-what-if-analysis-2/ on July 5, 2018.

Thabane et al, A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Medical Research Methodology201313:92

https://doi.org/10.1186/1471-2288-13-92. Retrieved from https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-13-92 on July 5th, 2018

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