# Probability Calculators for Decision-Making

### Tools for interpreting results of field surveys

Field practitioners in humanitarian settings often face challenges analyzing and interpreting the results of nutrition surveys. The key goal when analyzing and interpreting survey data is often to infer how high or low the true population prevalence is likely to be, and how likely is it to exceed the pre-determined action thresholds (e.g., 5%, 10%, 15% for GAM; 20% and 40% for anemia; etc.) To provide additional information for decision-making, we developed a “threshold” probability calculator that provides the estimated probability of the true population prevalence exceeding the threshold.

Another challenge for field practitioners is presented when the situation requires assessing significance of the difference between two survey results. For example, consider testing the difference between the surveys conducted in the same area in 2 different seasons or in 2 different years; or testing the differences between the results obtained from the surveys in 2 neighboring districts or livelihood zones. To assist field practitioners in these situations, we developed a “two-survey” calculator for testing the statistical significance of the difference between the estimates from 2 surveys (or from 2 strata of the same survey).

These calculators can be used for any categorical variable for which results are expressed as a proportion (or percentage) of the total – for example, for prevalence of anemia, immunization coverage, stunting, wasting, etc. The threshold levels in the “threshold” calculator can be changed as necessary for a given indicator. For example, it is possible to test what is the probability that measles immunization coverage exceeds a minimum acceptable level, or whether anemia prevalence exceeds programmatic action threshold that calls for blanket iron supplementation, etc.

We wanted to emphasize that analyses performed by these calculators can also be performed using any common statistical software, like SPSS, SAS or STATA. We propose them solely for their convenience, realizing that field practitioners often do not have advanced skills in data management and analysis, or do not have access to statistical software that require expensive licensing rights.

We look forward to a feedback from field practitioners on the use of these tools. Please send your questions, comments or suggestions to Dr. Oleg Bilukha: obilukha1@cdc.gov