PRICE-02: the price-framing replication with Sol and Terra
In this replication of PRICE-01, GPT-6 Sol and GPT-5.6 Terra each named a higher price under the gain frame. Sol’s observed mean was $24.36 under gain and $14.08 under loss; Terra’s was $18.95 and $9.37. The main collection is closed: 153 of 160 scheduled sessions were complete and eligible. The planned exact tests find higher price and purchase likelihood under gain for each model (all four Holm-adjusted p values < .001). The price interaction is not significant (p = .732). Numerical inference is available below; rationale coding and the final signed study record remain pending.
Numerical inference available · coding and app integration pending
PRICE-01 remains the original Claude Opus 5 / Sonnet 5.5 study. PRICE-02 is the presentation label used here for its Sol/Terra replication. The frozen native campaign retains its original identifiers and records.
What was asked
The scenario and the order of the six participant turns were retained from PRICE-01. A new infection is expected to affect 900 people in a region where the participant lives. A pharmacy will sell Treatment A, a one-season preventive course. Similar courses sell for $25. If all 900 people take it, the gain frame says 300 will be protected; the loss frame says 600 will be left unprotected. These describe the same outcome.
The two-by-two design crosses Frame (gain, loss) with Model (GPT-6 Sol, GPT-5.6 Terra). The primary outcome is the maximum price for one course for oneself, in whole US dollars from 0 to 60. The secondary outcome is likelihood of buying at $25, from 1 (not at all likely) to 7 (very likely). A short price rationale and protected/unprotected count checks complete the instrument.
Two samples each scheduled 20 sessions per condition. The 40-session measurement pilot and four-session execution pilot are excluded from every main-study summary on this page. Complete six-turn sessions are eligible; seven protocol failures remain missing and were not replaced. This whole-session rule is stricter than PRICE-01’s per-outcome availability.
Numerical results
| Frame / Model | GPT-6 Sol | GPT-5.6 Terra |
|---|---|---|
| Gain frame | M = 24.36 SD = 2.61, n = 39 95% CI [23.51, 25.21] 1 of 40 missing | M = 18.95 SD = 8.10, n = 37 95% CI [16.25, 21.65] 3 of 40 missing |
| Loss frame | M = 14.08 SD = 7.86, n = 39 95% CI [11.53, 16.63] 1 of 40 missing | M = 9.37 SD = 4.07, n = 38 95% CI [8.03, 10.71] 2 of 40 missing |
Gain minus loss is $10.28 for Sol and $9.58 for Terra. Both frame comparisons are significant in the planned exact tests: Holm-adjusted p = 2.700 × 10−9 for Sol and 1.823 × 10−7 for Terra. The sample-blocked price interaction is not significant (p = .732), so the difference between their framing effects is not established.
Price response distribution and exact counts
Likelihood to buy at $25
| Frame / Model | GPT-6 Sol | GPT-5.6 Terra |
|---|---|---|
| Gain frame | M = 4.90 SD = 0.97, n = 39 95% CI [4.58, 5.21] 1 of 40 missing | M = 4.70 SD = 2.75, n = 37 95% CI [3.79, 5.62] 3 of 40 missing |
| Loss frame | M = 2.56 SD = 1.65, n = 39 95% CI [2.03, 3.10] 1 of 40 missing | M = 1.47 SD = 1.37, n = 38 95% CI [1.02, 1.92] 2 of 40 missing |
Sol’s mean likelihood was 4.90 under gain and 2.56 under loss, a difference of 2.33 points. Terra’s means were 4.70 and 1.47, a difference of 3.23 points. Both gain-over-loss comparisons are significant: Holm-adjusted p = 4.923 × 10−9 for Sol and 1.586 × 10−7 for Terra. Sol and Terra do not differ significantly under gain (p = .691), but do under loss (p = .00356). These ratings concern a stated intention in this scenario, not an observed purchase.
Likelihood response distribution and exact counts
What the rationales say
The five original rationale codes are retained. The final coding package contains 154 captured rationales: 153 from eligible sessions and one for audit only. Final Claude Sonnet 5 coding has not run; there are no code counts, code charts or agreement estimates to report. A pending code is not a zero.
Fresh calibration and both pilot coding stages were completed. The final transfer remains on hold pending explicit approval for the prepared rationale payload. The page and its downloads contain only numerical summaries, not rationale text.
| Code | Gain frame, GPT-6 Sol | Gain frame, GPT-5.6 Terra | Loss frame, GPT-6 Sol | Loss frame, GPT-5.6 Terra |
|---|---|---|---|---|
| Unprotected share lowers the price (SHORTFALL) | Pending | Pending | Pending | Pending |
| Some protection is worth paying for (PROTECTION_VALUE) | Pending | Pending | Pending | Pending |
| Framing recognised (FRAME_EQUIVALENCE) | Pending | Pending | Pending | Pending |
| $25 reference sets the price (REFERENCE_PRICE) | Pending | Pending | Pending | Pending |
| Price derived by arithmetic (CALCULATED_PRICE) | Pending | Pending | Pending | Pending |
Manipulation checks
Every eligible complete session returned 300 protected and 600 unprotected. Both checks were correct in 153 of 153 observed eligible answers. The seven protocol failures remain missing against the scheduled denominator of 160.
| Check | Expected answer | Correct / observed | Incorrect | Missing | Scheduled |
|---|---|---|---|---|---|
| Protected count | 300 | 153 / 153 | 0 | 7 | 160 |
| Unprotected count | 600 | 153 / 153 | 0 | 7 | 160 |
The two samples
The two samples are blocks within the same planned collection. They are separate groups of sessions, not repeated measurements or independent studies. The table and charts retain each sample’s actual count. No sample-specific hypothesis verdict is supplied.
| Sample | Gain frame, GPT-6 Sol | Gain frame, GPT-5.6 Terra | Loss frame, GPT-6 Sol | Loss frame, GPT-5.6 Terra |
|---|---|---|---|---|
| Sample 1 | M = 24.74 SD = 1.15, n = 19 | M = 18.58 SD = 8.17, n = 19 | M = 12.85 SD = 7.25, n = 20 | M = 9.37 SD = 4.13, n = 19 |
| Sample 2 | M = 24.00 SD = 3.48, n = 20 | M = 19.33 SD = 8.25, n = 18 | M = 15.37 SD = 8.46, n = 19 | M = 9.37 SD = 4.13, n = 19 |
Sample 1: Maximum price (US dollars) — bar and line charts
Sample 2: Maximum price (US dollars) — bar and line charts
| Sample | Gain frame, GPT-6 Sol | Gain frame, GPT-5.6 Terra | Loss frame, GPT-6 Sol | Loss frame, GPT-5.6 Terra |
|---|---|---|---|---|
| Sample 1 | M = 5.16 SD = 0.76, n = 19 | M = 4.47 SD = 2.78, n = 19 | M = 2.25 SD = 1.59, n = 20 | M = 1.58 SD = 1.39, n = 19 |
| Sample 2 | M = 4.65 SD = 1.09, n = 20 | M = 4.94 SD = 2.78, n = 18 | M = 2.89 SD = 1.70, n = 19 | M = 1.37 SD = 1.38, n = 19 |
Sample 1: Likelihood to buy at $25 (1–7) — bar and line charts
Sample 2: Likelihood to buy at $25 (1–7) — bar and line charts
Inferential statistics
These are the frozen plan’s numerical tests on 153 eligible sessions. All four cells meet the 90% valid/scheduled threshold, and all 160 scheduled sessions are accounted for. The code outcomes and final signed study record remain pending; these tables are a partial numerical analysis.
Each outcome has four two-sided exact permutation tests, permuting within the two sample blocks. Their statistic is the absolute sum of the two within-sample mean differences. The displayed pooled mean difference is a descriptive effect size and need not equal half that statistic when counts differ. Holm adjustment includes all four comparisons within each outcome, separately for price and likelihood. No comparison was dropped from either family.
| Comparison | First n | Second n | Pooled mean difference | Signed sum of sample differences | Raw p | Holm p |
|---|---|---|---|---|---|---|
| Gain − loss, GPT-6 Sol | 39 | 39 | 10.282 | 20.518 | 6.75e-10 | 2.7e-09 |
| Gain − loss, GPT-5.6 Terra | 37 | 38 | 9.578 | 19.175 | 6.08e-08 | 1.82e-07 |
| Sol − Terra, gain frame | 39 | 37 | 5.413 | 10.825 | 0.000155 | 0.00031 |
| Sol − Terra, loss frame | 39 | 38 | 4.709 | 9.482 | 0.002 | 0.002 |
| Comparison | First n | Second n | Pooled mean difference | Signed sum of sample differences | Raw p | Holm p |
|---|---|---|---|---|---|---|
| Gain − loss, GPT-6 Sol | 39 | 39 | 2.333 | 4.663 | 1.23e-09 | 4.92e-09 |
| Gain − loss, GPT-5.6 Terra | 37 | 38 | 3.229 | 6.471 | 5.29e-08 | 1.59e-07 |
| Sol − Terra, gain frame | 39 | 37 | 0.195 | 0.390 | 0.691 | 0.691 |
| Sol − Terra, loss frame | 39 | 38 | 1.090 | 2.197 | 0.002 | 0.004 |
Differences are in US dollars for price and rating points for likelihood. The exact test compares absolute values of the signed sample sum; signs are retained here to make direction readable. The permutation method does not supply a conventional standard error, t statistic, degrees of freedom or contrast confidence interval, so none is invented. Full-precision p values and exact allocation totals are in the numerical JSON download.
| Term | b (US dollars) | SE | t | df | p | 95% CI lower | 95% CI upper |
|---|---|---|---|---|---|---|---|
| main effect of Frame | 9.926 | 0.992 | 10.007 | 148 | 2.52e-18 | 7.966 | 11.886 |
| main effect of Model | 5.056 | 0.992 | 5.098 | 148 | 1.04e-06 | 3.096 | 7.017 |
| interaction | 0.680 | 1.984 | 0.343 | 148 | 0.732 | -3.241 | 4.601 |
The declared price model includes a sample intercept, Frame, Model and their interaction. Gain and Sol are coded +0.5; loss and Terra −0.5. The three Wald t tests use 148 residual degrees of freedom. Their p values are unadjusted, as declared. The coefficient intervals are supplementary, unadjusted 95% Wald t intervals.
The price interaction is b = 0.680, t(148) = 0.343, p = .732. This provides no significant evidence that the two models’ price-framing effects differ; it does not establish equivalence. No omnibus model for likelihood was registered, and none is added here.
Independent arithmetic verification
Independent enumeration reproduced all 32 observed and sensitivity exact-test probabilities and all eight Holm families. An independent high-precision calculation found a small approximation error in the unchanged shared Student t routine: at most 8.07 × 10−9 relative difference in the model p values and 3.59 × 10−9 absolute difference in coefficient interval endpoints. This changes neither the displayed values nor any .05 decision. The frozen calculation is retained.
| ID | Model | Outcome | Prediction | Holm p | Numerical result |
|---|---|---|---|---|---|
| H1a | GPT-5.6 Terra | Maximum price | Gain > loss | 1.82e-07 | Criterion met |
| H1b | GPT-6 Sol | Maximum price | Gain > loss | 2.7e-09 | Criterion met |
| H2a | GPT-5.6 Terra | Likelihood to buy | Gain > loss | 1.59e-07 | Criterion met |
| H2b | GPT-6 Sol | Likelihood to buy | Gain > loss | 4.92e-09 | Criterion met |
All four numerical hypotheses meet their frozen criterion: a significant effect in the predicted direction at Holm-adjusted α = .05. This is the mechanical numerical result, not a claim that the full study has been finalized or admitted into the OpenPsy app. The original Opus-versus-Sonnet interaction hypothesis H1c was not transferred to these models.
All pooled and sample-specific means with 95% confidence intervals
| Outcome | Sample | Model | Frame | n | Mean | SD | 95% CI lower | 95% CI upper |
|---|---|---|---|---|---|---|---|---|
| Maximum price (USD) | Pooled | GPT-6 Sol | Gain frame | 39 | 24.359 | 2.611 | 23.513 | 25.205 |
| Maximum price (USD) | Pooled | GPT-5.6 Terra | Gain frame | 37 | 18.946 | 8.100 | 16.245 | 21.647 |
| Maximum price (USD) | Pooled | GPT-6 Sol | Loss frame | 39 | 14.077 | 7.862 | 11.528 | 16.625 |
| Maximum price (USD) | Pooled | GPT-5.6 Terra | Loss frame | 38 | 9.368 | 4.070 | 8.031 | 10.706 |
| Maximum price (USD) | Sample 1 | GPT-6 Sol | Gain frame | 19 | 24.737 | 1.147 | 24.184 | 25.290 |
| Maximum price (USD) | Sample 1 | GPT-5.6 Terra | Gain frame | 19 | 18.579 | 8.167 | 14.643 | 22.515 |
| Maximum price (USD) | Sample 1 | GPT-6 Sol | Loss frame | 20 | 12.850 | 7.250 | 9.457 | 16.243 |
| Maximum price (USD) | Sample 1 | GPT-5.6 Terra | Loss frame | 19 | 9.368 | 4.126 | 7.380 | 11.357 |
| Maximum price (USD) | Sample 2 | GPT-6 Sol | Gain frame | 20 | 24 | 3.479 | 22.372 | 25.628 |
| Maximum price (USD) | Sample 2 | GPT-5.6 Terra | Gain frame | 18 | 19.333 | 8.246 | 15.233 | 23.434 |
| Maximum price (USD) | Sample 2 | GPT-6 Sol | Loss frame | 19 | 15.368 | 8.460 | 11.291 | 19.446 |
| Maximum price (USD) | Sample 2 | GPT-5.6 Terra | Loss frame | 19 | 9.368 | 4.126 | 7.380 | 11.357 |
| Likelihood (1–7) | Pooled | GPT-6 Sol | Gain frame | 39 | 4.897 | 0.968 | 4.584 | 5.211 |
| Likelihood (1–7) | Pooled | GPT-5.6 Terra | Gain frame | 37 | 4.703 | 2.747 | 3.787 | 5.619 |
| Likelihood (1–7) | Pooled | GPT-6 Sol | Loss frame | 39 | 2.564 | 1.651 | 2.029 | 3.099 |
| Likelihood (1–7) | Pooled | GPT-5.6 Terra | Loss frame | 38 | 1.474 | 1.370 | 1.023 | 1.924 |
| Likelihood (1–7) | Sample 1 | GPT-6 Sol | Gain frame | 19 | 5.158 | 0.765 | 4.789 | 5.526 |
| Likelihood (1–7) | Sample 1 | GPT-5.6 Terra | Gain frame | 19 | 4.474 | 2.776 | 3.136 | 5.812 |
| Likelihood (1–7) | Sample 1 | GPT-6 Sol | Loss frame | 20 | 2.250 | 1.585 | 1.508 | 2.992 |
| Likelihood (1–7) | Sample 1 | GPT-5.6 Terra | Loss frame | 19 | 1.579 | 1.387 | 0.910 | 2.247 |
| Likelihood (1–7) | Sample 2 | GPT-6 Sol | Gain frame | 20 | 4.650 | 1.089 | 4.140 | 5.160 |
| Likelihood (1–7) | Sample 2 | GPT-5.6 Terra | Gain frame | 18 | 4.944 | 2.775 | 3.564 | 6.325 |
| Likelihood (1–7) | Sample 2 | GPT-6 Sol | Loss frame | 19 | 2.895 | 1.696 | 2.077 | 3.712 |
| Likelihood (1–7) | Sample 2 | GPT-5.6 Terra | Loss frame | 19 | 1.368 | 1.383 | 0.702 | 2.035 |
Each interval is mean ± t(0.975, n − 1) × SD/√n. These are individual unadjusted mean intervals, not simultaneous intervals across conditions. The same calculation is used in OpenPsy’s mean figures. Interval overlap is not the significance test. A t interval may extend beyond the permitted response range; plots show its full extent rather than clipping it.
Missing-data sensitivity
The original grammar-only sensitivity rule covers invalid, refused and provider-truncated outcomes. None of the eligible numerical answers had those classifications. Its bounds and four decision-rule results therefore equal the observed analysis. Native whole-session failures are a different classification and must not be relabeled as provider truncation.
The separate native sensitivity includes all seven unavailable scheduled outcomes. It evaluates all missing values at the scale minimum, all at the maximum, and the assignment against the gain-over-loss prediction: missing gain outcomes at the minimum and missing loss outcomes at the maximum. Every scenario reruns both complete four-comparison Holm families. These are endpoint assumptions, not imputed observations or confidence intervals.
| Scenario | ID | Model | Outcome | Holm p | Numerical result |
|---|---|---|---|---|---|
| All unavailable at minimum | H1a | GPT-5.6 Terra | Maximum price | 7.15e-06 | Criterion met |
| All unavailable at minimum | H1b | GPT-6 Sol | Maximum price | 3.6e-08 | Criterion met |
| All unavailable at minimum | H2a | GPT-5.6 Terra | Likelihood to buy | 7.3e-07 | Criterion met |
| All unavailable at minimum | H2b | GPT-6 Sol | Likelihood to buy | 2.15e-08 | Criterion met |
| All unavailable at maximum | H1a | GPT-5.6 Terra | Maximum price | 0.000972 | Criterion met |
| All unavailable at maximum | H1b | GPT-6 Sol | Maximum price | 1.6e-06 | Criterion met |
| All unavailable at maximum | H2a | GPT-5.6 Terra | Likelihood to buy | 9.22e-07 | Criterion met |
| All unavailable at maximum | H2b | GPT-6 Sol | Likelihood to buy | 2.62e-08 | Criterion met |
| Against the gain > loss prediction | H1a | GPT-5.6 Terra | Maximum price | 0.039 | Criterion met |
| Against the gain > loss prediction | H1b | GPT-6 Sol | Maximum price | 2.78e-05 | Criterion met |
| Against the gain > loss prediction | H2a | GPT-5.6 Terra | Likelihood to buy | 2.51e-05 | Criterion met |
| Against the gain > loss prediction | H2b | GPT-6 Sol | Likelihood to buy | 2.92e-07 | Criterion met |
All four criteria remain met in these endpoint scenarios. The least-favourable Terra price comparison is closest to the threshold, with Holm-adjusted p = .039. These bounds address the seven missing numerical outcomes under the stated range assumptions; they do not address route differences, unverified provider controls or other sources of bias.
| Outcome | Model | Frame | Observed n | Missing | Observed mean | Lower bound | Upper bound |
|---|---|---|---|---|---|---|---|
| Maximum price (USD) | GPT-6 Sol | Gain frame | 39 | 1 | 24.359 | 23.750 | 25.250 |
| Maximum price (USD) | GPT-5.6 Terra | Gain frame | 37 | 3 | 18.946 | 17.525 | 22.025 |
| Maximum price (USD) | GPT-6 Sol | Loss frame | 39 | 1 | 14.077 | 13.725 | 15.225 |
| Maximum price (USD) | GPT-5.6 Terra | Loss frame | 38 | 2 | 9.368 | 8.900 | 11.900 |
| Likelihood (1–7) | GPT-6 Sol | Gain frame | 39 | 1 | 4.897 | 4.800 | 4.950 |
| Likelihood (1–7) | GPT-5.6 Terra | Gain frame | 37 | 3 | 4.703 | 4.425 | 4.875 |
| Likelihood (1–7) | GPT-6 Sol | Loss frame | 39 | 1 | 2.564 | 2.525 | 2.675 |
| Likelihood (1–7) | GPT-5.6 Terra | Loss frame | 38 | 2 | 1.474 | 1.450 | 1.750 |
Every sensitivity comparison and price-model test
| Scenario | Outcome | Comparison | First n | Second n | Pooled mean difference | Signed sum of sample differences | Raw p | Holm p |
|---|---|---|---|---|---|---|---|---|
| all-minimum | Maximum price (USD) | Gain − loss, GPT-6 Sol | 40 | 40 | 10.025 | 20.050 | 9e-09 | 3.6e-08 |
| all-minimum | Maximum price (USD) | Gain − loss, GPT-5.6 Terra | 40 | 40 | 8.625 | 17.250 | 2.38e-06 | 7.15e-06 |
| all-minimum | Maximum price (USD) | Sol − Terra, gain frame | 40 | 40 | 6.225 | 12.450 | 0.000274 | 0.000547 |
| all-minimum | Maximum price (USD) | Sol − Terra, loss frame | 40 | 40 | 4.825 | 9.650 | 0.001 | 0.001 |
| all-minimum | Likelihood (1–7) | Gain − loss, GPT-6 Sol | 40 | 40 | 2.275 | 4.550 | 5.38e-09 | 2.15e-08 |
| all-minimum | Likelihood (1–7) | Gain − loss, GPT-5.6 Terra | 40 | 40 | 2.975 | 5.950 | 2.43e-07 | 7.3e-07 |
| all-minimum | Likelihood (1–7) | Sol − Terra, gain frame | 40 | 40 | 0.375 | 0.750 | 0.470 | 0.470 |
| all-minimum | Likelihood (1–7) | Sol − Terra, loss frame | 40 | 40 | 1.075 | 2.150 | 0.002 | 0.005 |
| all-maximum | Maximum price (USD) | Gain − loss, GPT-6 Sol | 40 | 40 | 10.025 | 20.050 | 4e-07 | 1.6e-06 |
| all-maximum | Maximum price (USD) | Gain − loss, GPT-5.6 Terra | 40 | 40 | 10.125 | 20.250 | 0.000324 | 0.000972 |
| all-maximum | Maximum price (USD) | Sol − Terra, gain frame | 40 | 40 | 3.225 | 6.450 | 0.191 | 0.382 |
| all-maximum | Maximum price (USD) | Sol − Terra, loss frame | 40 | 40 | 3.325 | 6.650 | 0.204 | 0.382 |
| all-maximum | Likelihood (1–7) | Gain − loss, GPT-6 Sol | 40 | 40 | 2.275 | 4.550 | 6.54e-09 | 2.62e-08 |
| all-maximum | Likelihood (1–7) | Gain − loss, GPT-5.6 Terra | 40 | 40 | 3.125 | 6.250 | 3.07e-07 | 9.22e-07 |
| all-maximum | Likelihood (1–7) | Sol − Terra, gain frame | 40 | 40 | 0.075 | 0.150 | 0.914 | 0.914 |
| all-maximum | Likelihood (1–7) | Sol − Terra, loss frame | 40 | 40 | 0.925 | 1.850 | 0.028 | 0.056 |
| gain-minimum-loss-maximum | Maximum price (USD) | Gain − loss, GPT-6 Sol | 40 | 40 | 8.525 | 17.050 | 6.95e-06 | 2.78e-05 |
| gain-minimum-loss-maximum | Maximum price (USD) | Gain − loss, GPT-5.6 Terra | 40 | 40 | 5.625 | 11.250 | 0.020 | 0.039 |
| gain-minimum-loss-maximum | Maximum price (USD) | Sol − Terra, gain frame | 40 | 40 | 6.225 | 12.450 | 0.000274 | 0.000821 |
| gain-minimum-loss-maximum | Maximum price (USD) | Sol − Terra, loss frame | 40 | 40 | 3.325 | 6.650 | 0.204 | 0.204 |
| gain-minimum-loss-maximum | Likelihood (1–7) | Gain − loss, GPT-6 Sol | 40 | 40 | 2.125 | 4.250 | 7.31e-08 | 2.92e-07 |
| gain-minimum-loss-maximum | Likelihood (1–7) | Gain − loss, GPT-5.6 Terra | 40 | 40 | 2.675 | 5.350 | 8.36e-06 | 2.51e-05 |
| gain-minimum-loss-maximum | Likelihood (1–7) | Sol − Terra, gain frame | 40 | 40 | 0.375 | 0.750 | 0.470 | 0.470 |
| gain-minimum-loss-maximum | Likelihood (1–7) | Sol − Terra, loss frame | 40 | 40 | 0.925 | 1.850 | 0.028 | 0.056 |
| Scenario | Term | b | SE | t | df | p |
|---|---|---|---|---|---|---|
| all-minimum | main effect of Frame | 9.325 | 1.100 | 8.474 | 155 | 1.72e-14 |
| all-minimum | main effect of Model | 5.525 | 1.100 | 5.021 | 155 | 1.4e-06 |
| all-minimum | interaction | 1.400 | 2.201 | 0.636 | 155 | 0.526 |
| all-maximum | main effect of Frame | 10.075 | 1.721 | 5.854 | 155 | 2.77e-08 |
| all-maximum | main effect of Model | 3.275 | 1.721 | 1.903 | 155 | 0.059 |
| all-maximum | interaction | -0.100 | 3.442 | -0.029 | 155 | 0.977 |
| gain-minimum-loss-maximum | main effect of Frame | 7.075 | 1.504 | 4.705 | 155 | 5.59e-06 |
| gain-minimum-loss-maximum | main effect of Model | 4.775 | 1.504 | 3.175 | 155 | 0.002 |
| gain-minimum-loss-maximum | interaction | 2.900 | 3.008 | 0.964 | 155 | 0.336 |
What these results support
The four retained gain-over-loss hypotheses meet their numerical decision rule for both maximum price and likelihood to buy. That statement is restricted to the collected sessions, wording and native route. The price interaction is not significant; a non-significant interaction does not establish equal effects.
The missing-data endpoint scenarios retain all four numerical criteria, although the least-favourable Terra price comparison is close to the threshold at Holm-adjusted p = .039. No rationale-based explanation is supplied until the planned coding is complete.
The native campaign remains outside the completed standard Library/app workflow. This partial numerical report does not substitute for completed coding, a fresh signed independent analysis review or final study recording.
How the run went
The 40-session measurement pilot and four-session execution pilot completed, with fresh calibration, collection review and coding. The fixed main schedule then processed all 160 sessions. There are 153 eligible complete sessions and seven known protocol failures, with no unknown or unattempted main-study sessions and no replacements. Protocol rejection is not by itself evidence of a scientific model refusal.
This was a source-pinned native successor with fresh plan, execution and independent-review records. It was not the unchanged original OpenPsy app import-and-run path. The differences below remain part of its interpretation and cannot be repaired by renaming or reformatting the page.
| Area | Native replication record |
|---|---|
| App and Library | The preserved source was checked with the original compiler and canonical Flow simulator. The current Library compiler rejected the historical executableVersion field. This campaign was not imported through the current Library UI. |
| Participant route | Separate native Codex execution replaced the Claude Code route. Requested and configured model names were observed; the serving model snapshot was not independently verified. |
| Reply limit | The original 1,024-token cap could not be sent as a cap parameter through this native API. No extra length instruction was inserted. Observed usage does not verify provider truncation or an enforced limit. |
| Reasoning, randomness and context | Low reasoning effort was requested. Effective compute, temperature, top-p, account isolation and complete upstream context were not independently verified. Observed tool activity caused protocol refusal. |
| Execution and eligibility | Sessions ran sequentially in the committed randomized order. Only complete six-turn sessions were eligible; no missing main-study session was replaced. PRICE-01 used concurrency four and per-outcome availability. |
| Administrative second attempt | A separately reviewed pre-response startup attempt retained one unknown claimed row and 39 unattempted rows, with zero application participant requests. One prospective administrative successor was approved. This is a substantive exception to unchanged no-repeat compliance. |
| Pilot-result exposure | Pilot cell means and contrasts, then four pilot rationale texts, were viewed before confirmatory finalization and disclosed. The pre-participant scientific commitments remained unchanged; reviewer blinding is not claimed. |
| Pilot gates and power | The observable native completion endpoint replaced the unavailable provider-truncation gate. Original planning distributions were proxies; target-model power was not validated. |
| Coding and analysis | Fresh single-coder calibration and both pilots passed their recorded gates. Scalar inference and both numerical sensitivity analyses have been computed under the frozen rules. Final code analyses, signed study recording and app result integration remain incomplete. |
How this relates to PRICE-01
PRICE-01 is the original Claude Opus 5 / Claude Sonnet 5.5 study, run on 6 October 2026. It has a saved OpenPsy study, recorded analysis and a published results page. Its price outcome used 157 available responses, its likelihood outcome 156, and its coded rationales 154; those denominators are specific to the original study.
PRICE-02 is the review label for the GPT-6 Sol / GPT-5.6 Terra replication. The retained campaign identifier is PRICE01-SOL-TERRA-20261007. Historical paths, signed records and source hashes continue to use that original identifier. This presentation mapping does not create a new registered app study or retroactively change collection.
Read the two studies separately: participant models, route controls, context, concurrency and eligibility differ. A comparison between the pages is descriptive and does not isolate a model or provider effect.
Tables and downloads
Download all PRICE-02 figures and tables · Verified descriptive data (JSON)
- Condition summaries (CSV)
- Sample summaries (CSV)
- Gain minus loss differences (CSV)
- Observed response distributions (CSV)
- Manipulation checks (CSV)
Full-precision numerical inference (JSON)
- Maximum price: four exact sample-stratified comparisons (CSV)
- Likelihood to buy: separate four-comparison family (CSV)
- Maximum price: sample-blocked linear model (CSV)
- Numerical decision-rule results; final study recording pending (CSV)
- Cell means and individual 95% confidence intervals (CSV)
- Native missingness: all four numerical criteria under each scenario (CSV)
- Native all-unavailable bounds on each cell mean (CSV)
- All 24 exact comparisons in the three native endpoint scenarios (CSV)
- All nine price-model terms in the three native endpoint scenarios (CSV)
Condition summaries — full table
| Model | Frame | Outcome | Unit | Scheduled | Analysed | Missing | Mean | Sample SD |
|---|---|---|---|---|---|---|---|---|
| GPT-6 Sol | Gain frame | Maximum price | US dollars | 40 | 39 | 1 | 24.36 | 2.61 |
| GPT-6 Sol | Gain frame | Likelihood to buy | 1 to 7 | 40 | 39 | 1 | 4.90 | 0.97 |
| GPT-6 Sol | Loss frame | Maximum price | US dollars | 40 | 39 | 1 | 14.08 | 7.86 |
| GPT-6 Sol | Loss frame | Likelihood to buy | 1 to 7 | 40 | 39 | 1 | 2.56 | 1.65 |
| GPT-5.6 Terra | Gain frame | Maximum price | US dollars | 40 | 37 | 3 | 18.95 | 8.10 |
| GPT-5.6 Terra | Gain frame | Likelihood to buy | 1 to 7 | 40 | 37 | 3 | 4.70 | 2.75 |
| GPT-5.6 Terra | Loss frame | Maximum price | US dollars | 40 | 38 | 2 | 9.37 | 4.07 |
| GPT-5.6 Terra | Loss frame | Likelihood to buy | 1 to 7 | 40 | 38 | 2 | 1.47 | 1.37 |
Sample summaries — full table
| Sample | Model | Frame | Outcome | Unit | Scheduled | Analysed | Missing | Mean | Sample SD |
|---|---|---|---|---|---|---|---|---|---|
| 1 | GPT-6 Sol | Gain frame | Maximum price | US dollars | 20 | 19 | 1 | 24.74 | 1.15 |
| 1 | GPT-6 Sol | Gain frame | Likelihood to buy | 1 to 7 | 20 | 19 | 1 | 5.16 | 0.76 |
| 1 | GPT-6 Sol | Loss frame | Maximum price | US dollars | 20 | 20 | 0 | 12.85 | 7.25 |
| 1 | GPT-6 Sol | Loss frame | Likelihood to buy | 1 to 7 | 20 | 20 | 0 | 2.25 | 1.59 |
| 1 | GPT-5.6 Terra | Gain frame | Maximum price | US dollars | 20 | 19 | 1 | 18.58 | 8.17 |
| 1 | GPT-5.6 Terra | Gain frame | Likelihood to buy | 1 to 7 | 20 | 19 | 1 | 4.47 | 2.78 |
| 1 | GPT-5.6 Terra | Loss frame | Maximum price | US dollars | 20 | 19 | 1 | 9.37 | 4.13 |
| 1 | GPT-5.6 Terra | Loss frame | Likelihood to buy | 1 to 7 | 20 | 19 | 1 | 1.58 | 1.39 |
| 2 | GPT-6 Sol | Gain frame | Maximum price | US dollars | 20 | 20 | 0 | 24 | 3.48 |
| 2 | GPT-6 Sol | Gain frame | Likelihood to buy | 1 to 7 | 20 | 20 | 0 | 4.65 | 1.09 |
| 2 | GPT-6 Sol | Loss frame | Maximum price | US dollars | 20 | 19 | 1 | 15.37 | 8.46 |
| 2 | GPT-6 Sol | Loss frame | Likelihood to buy | 1 to 7 | 20 | 19 | 1 | 2.89 | 1.70 |
| 2 | GPT-5.6 Terra | Gain frame | Maximum price | US dollars | 20 | 18 | 2 | 19.33 | 8.25 |
| 2 | GPT-5.6 Terra | Gain frame | Likelihood to buy | 1 to 7 | 20 | 18 | 2 | 4.94 | 2.78 |
| 2 | GPT-5.6 Terra | Loss frame | Maximum price | US dollars | 20 | 19 | 1 | 9.37 | 4.13 |
| 2 | GPT-5.6 Terra | Loss frame | Likelihood to buy | 1 to 7 | 20 | 19 | 1 | 1.37 | 1.38 |
Gain minus loss differences — full table
| Sample | Model | Outcome | Unit | Gain N | Loss N | Difference |
|---|---|---|---|---|---|---|
| Pooled | GPT-6 Sol | Maximum price | US dollars | 39 | 39 | 10.28 |
| Pooled | GPT-6 Sol | Likelihood to buy | scale points | 39 | 39 | 2.33 |
| Pooled | GPT-5.6 Terra | Maximum price | US dollars | 37 | 38 | 9.58 |
| Pooled | GPT-5.6 Terra | Likelihood to buy | scale points | 37 | 38 | 3.23 |
| 1 | GPT-6 Sol | Maximum price | US dollars | 19 | 20 | 11.89 |
| 1 | GPT-6 Sol | Likelihood to buy | scale points | 19 | 20 | 2.91 |
| 1 | GPT-5.6 Terra | Maximum price | US dollars | 19 | 19 | 9.21 |
| 1 | GPT-5.6 Terra | Likelihood to buy | scale points | 19 | 19 | 2.89 |
| 2 | GPT-6 Sol | Maximum price | US dollars | 20 | 19 | 8.63 |
| 2 | GPT-6 Sol | Likelihood to buy | scale points | 20 | 19 | 1.76 |
| 2 | GPT-5.6 Terra | Maximum price | US dollars | 18 | 19 | 9.96 |
| 2 | GPT-5.6 Terra | Likelihood to buy | scale points | 18 | 19 | 3.58 |
Observed response distributions — full table
| Model | Frame | Outcome | Value | Count |
|---|---|---|---|---|
| GPT-6 Sol | Gain frame | Maximum price | 0 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 1 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 2 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 3 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 4 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 5 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 6 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 7 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 8 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 9 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 10 | 1 |
| GPT-6 Sol | Gain frame | Maximum price | 11 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 12 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 13 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 14 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 15 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 16 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 17 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 18 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 19 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 20 | 2 |
| GPT-6 Sol | Gain frame | Maximum price | 21 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 22 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 23 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 24 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 25 | 36 |
| GPT-6 Sol | Gain frame | Maximum price | 26 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 27 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 28 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 29 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 30 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 31 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 32 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 33 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 34 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 35 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 36 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 37 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 38 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 39 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 40 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 41 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 42 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 43 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 44 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 45 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 46 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 47 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 48 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 49 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 50 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 51 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 52 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 53 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 54 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 55 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 56 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 57 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 58 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 59 | 0 |
| GPT-6 Sol | Gain frame | Maximum price | 60 | 0 |
| GPT-6 Sol | Gain frame | Likelihood to buy | 1 | 1 |
| GPT-6 Sol | Gain frame | Likelihood to buy | 2 | 0 |
| GPT-6 Sol | Gain frame | Likelihood to buy | 3 | 2 |
| GPT-6 Sol | Gain frame | Likelihood to buy | 4 | 4 |
| GPT-6 Sol | Gain frame | Likelihood to buy | 5 | 24 |
| GPT-6 Sol | Gain frame | Likelihood to buy | 6 | 8 |
| GPT-6 Sol | Gain frame | Likelihood to buy | 7 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 0 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 1 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 2 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 3 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 4 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 5 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 6 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 7 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 8 | 18 |
| GPT-6 Sol | Loss frame | Maximum price | 9 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 10 | 8 |
| GPT-6 Sol | Loss frame | Maximum price | 11 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 12 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 13 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 14 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 15 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 16 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 17 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 18 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 19 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 20 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 21 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 22 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 23 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 24 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 25 | 13 |
| GPT-6 Sol | Loss frame | Maximum price | 26 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 27 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 28 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 29 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 30 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 31 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 32 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 33 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 34 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 35 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 36 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 37 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 38 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 39 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 40 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 41 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 42 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 43 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 44 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 45 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 46 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 47 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 48 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 49 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 50 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 51 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 52 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 53 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 54 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 55 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 56 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 57 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 58 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 59 | 0 |
| GPT-6 Sol | Loss frame | Maximum price | 60 | 0 |
| GPT-6 Sol | Loss frame | Likelihood to buy | 1 | 14 |
| GPT-6 Sol | Loss frame | Likelihood to buy | 2 | 12 |
| GPT-6 Sol | Loss frame | Likelihood to buy | 3 | 0 |
| GPT-6 Sol | Loss frame | Likelihood to buy | 4 | 3 |
| GPT-6 Sol | Loss frame | Likelihood to buy | 5 | 10 |
| GPT-6 Sol | Loss frame | Likelihood to buy | 6 | 0 |
| GPT-6 Sol | Loss frame | Likelihood to buy | 7 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 0 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 1 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 2 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 3 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 4 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 5 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 6 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 7 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 8 | 12 |
| GPT-5.6 Terra | Gain frame | Maximum price | 9 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 10 | 1 |
| GPT-5.6 Terra | Gain frame | Maximum price | 11 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 12 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 13 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 14 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 15 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 16 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 17 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 18 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 19 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 20 | 1 |
| GPT-5.6 Terra | Gain frame | Maximum price | 21 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 22 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 23 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 24 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 25 | 23 |
| GPT-5.6 Terra | Gain frame | Maximum price | 26 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 27 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 28 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 29 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 30 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 31 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 32 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 33 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 34 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 35 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 36 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 37 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 38 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 39 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 40 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 41 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 42 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 43 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 44 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 45 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 46 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 47 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 48 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 49 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 50 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 51 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 52 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 53 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 54 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 55 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 56 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 57 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 58 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 59 | 0 |
| GPT-5.6 Terra | Gain frame | Maximum price | 60 | 0 |
| GPT-5.6 Terra | Gain frame | Likelihood to buy | 1 | 11 |
| GPT-5.6 Terra | Gain frame | Likelihood to buy | 2 | 2 |
| GPT-5.6 Terra | Gain frame | Likelihood to buy | 3 | 1 |
| GPT-5.6 Terra | Gain frame | Likelihood to buy | 4 | 0 |
| GPT-5.6 Terra | Gain frame | Likelihood to buy | 5 | 0 |
| GPT-5.6 Terra | Gain frame | Likelihood to buy | 6 | 5 |
| GPT-5.6 Terra | Gain frame | Likelihood to buy | 7 | 18 |
| GPT-5.6 Terra | Loss frame | Maximum price | 0 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 1 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 2 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 3 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 4 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 5 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 6 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 7 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 8 | 32 |
| GPT-5.6 Terra | Loss frame | Maximum price | 9 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 10 | 2 |
| GPT-5.6 Terra | Loss frame | Maximum price | 11 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 12 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 13 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 14 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 15 | 2 |
| GPT-5.6 Terra | Loss frame | Maximum price | 16 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 17 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 18 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 19 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 20 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 21 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 22 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 23 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 24 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 25 | 2 |
| GPT-5.6 Terra | Loss frame | Maximum price | 26 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 27 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 28 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 29 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 30 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 31 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 32 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 33 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 34 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 35 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 36 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 37 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 38 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 39 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 40 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 41 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 42 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 43 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 44 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 45 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 46 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 47 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 48 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 49 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 50 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 51 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 52 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 53 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 54 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 55 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 56 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 57 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 58 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 59 | 0 |
| GPT-5.6 Terra | Loss frame | Maximum price | 60 | 0 |
| GPT-5.6 Terra | Loss frame | Likelihood to buy | 1 | 30 |
| GPT-5.6 Terra | Loss frame | Likelihood to buy | 2 | 6 |
| GPT-5.6 Terra | Loss frame | Likelihood to buy | 3 | 0 |
| GPT-5.6 Terra | Loss frame | Likelihood to buy | 4 | 0 |
| GPT-5.6 Terra | Loss frame | Likelihood to buy | 5 | 0 |
| GPT-5.6 Terra | Loss frame | Likelihood to buy | 6 | 0 |
| GPT-5.6 Terra | Loss frame | Likelihood to buy | 7 | 2 |
Manipulation checks — full table
| Check | Expected | Scheduled | Valid | Correct | Incorrect | Missing |
|---|---|---|---|---|---|---|
| Protected count | 300 | 160 | 153 | 153 | 0 | 7 |
| Unprotected count | 600 | 160 | 153 | 153 | 0 | 7 |
Review and application status
This page presents the partial numerical results for review on OpenPsy. PRICE-01 remains the separate original study. Rationale coding, final signed recording and standard app integration remain pending.
The corrected page reuses the established site’s typography, section structure, two-by-two tables, model colours, figure grammar and accessible bar/line controls. It includes the planned scalar tests and their uncertainty/sensitivity tables. It does not claim that the native study has completed coding, final recording or the canonical app workflow.
| Item | Status |
|---|---|
| Original PRICE-01 | Saved OpenPsy app study and published recorded-results page |
| PRICE-02 collection | Closed: 153 complete eligible sessions from 160 scheduled |
| PRICE-02 Library/app study | Not imported into the standard Library/app workflow |
| PRICE-02 rationale coding | Final 154-rationale transfer and coding pending |
| PRICE-02 statistical report | Numerical exact tests, price-model terms, mean intervals, decision-rule results and endpoint sensitivities available; code families and final signed record pending |
| This page | Public review of the partial numerical results in the existing OpenPsy format |
Numerical provenance
Counts, means, sample SDs, distributions and numerical inference are derived from the independently verified 153-row projection. The scalar tests use the unchanged source-pinned analysis functions; the separate numerical result seal is sha256:a926f9489071713b43eafe086008a1e5c580c481d3f116afd3546fa7ae377a13. No missing value is converted to zero. Exact source identities:
collectionSha: sha256:20becb2c1d023cee79efc0d4bdfb4a3a015661ae23db8de02303ef5161eb41db
descriptiveResultSha: sha256:5c818006af3f012b099b8218eb41ebe721839875dd59758a54bdc8c39e6cde86
independentAccountingSha: sha256:75d70b113ead3971d4824c329447b06355f566a479bc635b122afd7dbb5f8a4f
numericProjectionSha: sha256:5734f9611949b57777d180349c92327e6727de420e975572a89d4d4969adbdf0