samediffPwr | R Documentation |

Computes the power for at same-different discrimination experiment with a no-difference null hypothesis via simulation.

```
samediffPwr(n = 1000, tau, delta, Ns, Nd, alpha = 0.05)
```

`n` |
the number of samples to use in the simulation. More samples means higher precision, but takes longer to compute. |

`tau` |
the value of tau |

`delta` |
the underlying sensory difference under the |

`Ns` |
the number of same-samples (a positive integer) |

`Nd` |
the number of different-samples (a positive integer) |

`alpha` |
the type I level of the test (must be between zero and one) |

The power is computed using simulations. `n`

datasets is
simulated from the
Same Different model with specified parameters. The power is the
fraction of times the p-value is lower than `alpha`

.

Under some parameter combinations, there is a non-significant
probability that data will fall, so that the MLE of `delta`

is
not defined and the p-value is not defined. All such undefined
p-values are silently ignored.

The estimated power may change between runs and especially if the power is either very large or very small (ie. close to 0 or 1). Using more simulations will provide higher accuracy.

It is often a good idea to run the power simulation a couple of times to ensure that the variation in the result is acceptable.

A single numeric value giving the power of the specified test.

Rune Haubo B Christensen

Christensen, R.H.B., Brockhoff, P.B. (2009). Estimation and inference in the same-different test. Food, Quality and Preference, 20 pp. 514–520

`samediff`

, `samediffSim`

```
## Finding the power of a discrimination test with a sensory delta of 2
## (alternative hypothesis) versus a null hypothesis of delta = 0 with
## a sample of size 2 x 10 and a type I level of .05. n should be higher
## for a reasonable precision:
samediffPwr(n = 100, tau = 1, delta = 2, Ns = 10, Nd = 10)
```

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