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1 | Triangle test | By: Justin Angevaare | |

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3 | Please "Make a copy..." of this sheet to use the calculator. You should find this option under the "File" menu as long as you are logged into your Google account. Cheers! | ||

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5 | One sided p-value calculator | ||

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7 | Number of correct participants | 6 | |

8 | Total number of participants | 16 | |

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10 | Exact p-value | 0.453 | |

11 | Approximate p-value | 0.362 | |

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13 | Background | ||

14 | The statistical analysis associated with the triangle test compares the proportion of test participants whom have correctly identified an odd beer out, to the proportion of tasters that would be expected to correctly identify the odd beer purely due to random chance. The greater the proportion of correct participants, the more evidence there is against the “random chance” null hypothesis. As we are only interested if the different beers can be correctly distinguished, this will be an “upper tailed” test. That is to say, the potential result of the odd beer out being correctly identified less than we’d expect under random chance isn’t of particular interest to us (and is not particularly likely either). | ||

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16 | In plain terms, our null hypothesis is that the true proportion of population that can correctly identify the odd beer out is 1/3. Our alternative hypothesis is that the true proportion of the population that can correctly identify the odd beer out is greater than 1/3. | ||

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18 | Exact p-value | ||

19 | The exact p-value can be calculated using the binomial distribution. Specifically, the p-value is found as the probability of having observed at least as many correct tasters, if the population proportion is infact equal to 1/3. | ||

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21 | Approximate p-value | ||

22 | An approximate p-value can be calculated assuming that under the null hypothesis, the estimated proportion will follow a normal distribution with mean equal to 1/3. The approximate method may be used when the sample size is equal or greater than 25. |

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