# Summary of changes made to *p*-curve app

Last update: 2024 08 28
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**App 4.10** (2024.08.28)
Fixed bug identified by Richard Morey where the half p-curve test for null of 33% power was too conservative, conditioning twice on p<.05.
The line of code changed is

- old: prop25 = 3*prop33(.025)
- new: prop25 = prop33(.025)

**App 4.06** (2017.11.30)** (R Code for 4.06)**
Added half p-curve result for flat test to figure legend and table beneath it.
**App 4.052** (2017.02.28)** (R Code for 4.052)**
More minor changes to accommodate APS strict copy-editing requirements (e.g., spaces before/after "=")
**App 4.051** (2017.02.24)
Minor changes to legend of figures to accommodate APS strict copy-editing requirements.
**App 4.05** (2016.06.17)
Modified main figure to accommodate journal styles. Test results now reported within figure legend and in separate table
rather than in embedded table.
**App 4.04** (2016.06.10)
Fixed bug that dropped green line for 33% power when including n.s. results
**App 4.03** (2016.04.08)
Expanded y-axis of diagnostic plot for power so that extreme results were still viewable
**App 4.02** (2016.03.30)
Fixed bug that p-values below precision level of R (p<2.2e-16) led algorithm that estimates power to crash
Fixed typo in number of studies total vs significant.
**App 4.01** (2016.03.06)
Fixed bug in computation of binomial test for 33% power
**App 4.00** (2016.01.10)
Most of the changes follow from our third paper on

*p*-curve: "Better P-Curves" (

SSRN)

Started reporting (90%) Confidence interval for power estimate (computed by finding the level of power that
leads to expected *p*-curves that lead the Stouffer test comparing observed with expected have p=.05 and p=.95)
Started reporting results for 'half *p*-curve' (*p*<.025 results)
New test of evidential value: is half *p*-curve *p*<.05 or both full and half p-curve p<.1?
New test of inadequate evidential value: is flatter than 33% power test *p*<.05 for full *p*-curve or *p*<.1 for both the half and binomial flatter than 33% power?
Interpretation of reported results, in terms of overall conclusions, printed below summary of results
Statistical results embedded within the summary figure.
Cumulative *p*-curve adjusts for exclusions, testing whether pp-values are uniform between the highest dropped pp-value and 1, rather than between 0 and 1.
Left-skew results no longer reported (the overall p-value for left skew is 1 minus the *p*-value for right-skew)
Power is estimated by finding the level of power that leads to an overall *p*-value for Stouffer's test of *p*=.5 rather
than by minimizing Kolmogorov-Smirnov D stat against uniform
Modified computation of 33% power for t-tests and Z-tests. Up to App3 power was computed for directional predictions (tested with two-sided tests),
starting with App4 non-directional ones. This ensures that t() & F(), and Z & chi(), obtain equivalent non-centrality parameters (since F() and chi() are not directional tests).
So up to App3, the noncentrality parameter would be set so that there is a 1/3 probability of obtaining a positive effect that is p<.05 (two sided).
Starting with App4, so that there is a 1/3 probability of obtaining a positive or negative effect that is p<.05 (two-sided).
**App 3.01** (2015.04.13)
Fixed bug with p-values smaller than precision level of R (p<2.2e-16)
**App 3.0**
Estimates publication bias corrected statistical power
Cumulative p-curve analyses, reports results without most extreme p-values
Detailed explanations for calculations using user entered data as example
Uses Stouffer instead of Fisher's method to aggregate *pp*-values
**App 2.0**
New user interface, instead of entering the attributes of each test one at a time,
they are all entered together into a large text-area following a pre-specified syntax.