This app is supporting material for:

Francis, G. (2017). Equivalent statistics and data interpretation, * Behavior Research Methods*, **49**(4), 1524-1538. The R software mentioned in the text is available at the Open Science Framework.

When you run a two-sample *t*-test you report a variety of statistics. What might not be obvious is that this reporting is often redundant because the statistics are mathematically related in a one-to-one fashion. Given the experimental sample sizes, this program takes any of the indicated statistics and uses algebra and calculus to compute any of the others. These calculations are one-to-one and exact. (Although there may be rounding error in the calculations reported here.) The ability to do these transformations highlights that each of these statistical terms contains equivalent information about whatever effect exists in the data set. Discussions about which term is most meaningful (e.g., *p*-value, effect size, confidence interval, Bayes Factor) **should not** be about the *information* in the statistic but about describing and interpreting that information.

Specify sample sizes:

n_{1}:
n_{2}:

Statistic | Value | |
---|---|---|

t-value | ||

p-value | ||

Cohen's d | ||

Lower limit for Cohen's d 95% confidence interval | ||

Upper limit for Cohen's d 95% confidence interval | ||

Hedge's g | ||

Lower limit for Hedges' g 95% confidence interval | ||

Upper limit for Hedges' g 95% confidence interval | ||

Post hoc power (from d) | ||

Post hoc power (from g) | ||

JZS Bayes Factor (alt / null) | (might take a long time) | |

Λ, log likelihood ratio (full / null) | (cannot be negative or zero) | |

ΔAIC (null - full) | ||

ΔAICc (null - full) | ||

ΔBIC (null - full) |