Two-Alternative Forced Choice (2-AFC)

Two-Alternative Forced Choice (2-AFC)


The Two-Alternative Forced Choice (2-AFC) method, is a specific way of comparing two samples in sensory science. In this method, the only allowed difference between the two samples is a specified sensory characteristic. It works well when the sensory scientist already knows that the samples differ in just one particular way.
However, if the scientist isn't sure which sensory attribute makes the samples different, other methods like the difference-paired comparison have to be used. The downside is that these alternatives might not be as powerful statistically.

Template Description

The template starts with a screen giving instructions to panellists. In the design section, the first screen has instructions, followed by a question where participants pick the more intense product in a specific attribute (e.g., sweetness). After choosing, panellists can add comments.
On the end screen, the template incorporates a thank-you message, providing an opportunity for you to personalize a final message for the panellists. 
Within the design settings, you can examine how each panellist will receive the sample, with each set corresponding to a specific order of presentation for an individual panellist.
Within this template, Product 1 functions as the reference product, while Product 2 represents the new formulation (e.g., one with a reduced sucrose level). You have the flexibility to edit these fields by clicking on the Product section.

Raw Data Representation

The raw data will be displayed, with each row corresponding to an individual judge and the pair of products they were presented with. The data will be stored to indicate whether the panellists correctly identified the odd sample or not. If the odd sample was not recognized, the data will be recorded as a 0, whereas if the panellists correctly identified the odd sample, the data will be recorded as a 1. The Q1_info column provides additional information about the products presented to the panellists, with the selection made by the panellists added in brackets.


Once data has been gathered, you can access EyeOpenR and navigate to the Discrimination methods. Choose the analysis to examine your results. Additional details about this analysis can be found in the following article:


  1. Reference: Lawless, H. T., & Heymann, H. (2010)

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