Higher Dark Chocolate Nuts & Sea Salt Consumption Predicts Very Slightly Lower Guiltiness for Population

Higher Dark Chocolate Nuts & Sea Salt Consumption Predicts Very Slightly Lower Guiltiness for Population

Contents

Variables

A
Dark Chocolate Nuts & Sea Salt 34
A
Guiltiness 2319

Categories

A
Foods 13415
A
Emotions 2028

Actions

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Your Data

Tags

Medium Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 0.6% average increase in Guiltiness following above average Dark Chocolate Nuts & Sea Salt Consumption.

Abstract

Guiltiness was generally 3.3% lower than average after 2 serving of Dark Chocolate Nuts & Sea Salt per 14 days.

Aggregated data from 2 study participants suggests with a MEDIUM degree of confidence (p=0.377, 95% CI -0.42 to 0.298) that Dark Chocolate Nuts & Sea Salt has a very weakly negative predictive relationship (R=-0.061) with Guiltiness.

The highest quartile of Guiltiness measurements were observed following an average 0.5 serving Dark Chocolate Nuts & Sea Salt per day.

The lowest quartile of Guiltiness measurements were observed following an average 0.506 serving of Dark Chocolate Nuts & Sea Salt per day.

After an onset delay of 30 minutes, Guiltiness is typically 3% lower than average over the 14 days following around 0.506 serving of Dark Chocolate Nuts & Sea Salt Dark Chocolate Nuts & Sea Salt.

Keywords: Dark Chocolate Nuts & Sea Salt, Guiltiness, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 2 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 137 paired observations from 2 participants revealed a minimal reduction in Guiltiness following above-average Dark Chocolate Nuts & Sea Salt exposure.

-3.3%
Change from Baseline
Minimal effect on Guiltiness
0.19
Predictor Impact Score
Weak evidence for causal relationship

Supporting Statistics

Medium
Confidence
-0.061
Correlation (r)
p = 0.048
Significance
z = 0.07
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Dark Chocolate Nuts & Sea Salt:

  • Guiltiness decreased by 3.3% on average
  • Temporal analysis supports Dark Chocolate Nuts & Sea Salt as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.048)

Interpreting the Predictor Impact Score

The Predictor Impact Score (PIS) integrates multiple Bradford Hill causal criteria into a single metric. Use this guide to interpret the score:

PIS Range Interpretation Recommended Action
≥ 0.5 Strong evidence High priority for RCT validation
0.3 - 0.5 Moderate evidence Consider for experimental investigation
0.1 - 0.3 Weak evidence Monitor for additional data
< 0.1 Insufficient evidence Low priority; may be noise

Note: PIS is a prioritization heuristic, not proof of causation. High scores indicate relationships worth investigating, not confirmed causal effects. With only 2 participants, these scores are preliminary and will become more reliable as additional data is collected.

Optimal Daily Values

No clear dose-response relationship detected. The Dark Chocolate Nuts & Sea Salt values associated with high and low Guiltiness are too similar to provide meaningful dosing guidance. This may indicate a threshold effect (any amount works equally well), no effect, or insufficient data variance. With more participants, a clearer pattern may emerge.

Population Correlation

Dark Chocolate Nuts & Sea Salt Distribution

Guiltiness Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Dark Chocolate Nuts & Sea Salt Consumption
Effect Variable Name Guiltiness
Sinn Predictive Coefficient 0.19232667110851
Confidence Level MEDIUM
Confidence Interval 0.35871
Forward Pearson Predictive Coefficient -0.061
Critical T Value 1.646
Total Dark Chocolate Nuts & Sea Salt Consumption Over Previous 14 days Before ABOVE Average Guiltiness 0.5 serving
Total Dark Chocolate Nuts & Sea Salt Consumption Over Previous 14 days Before BELOW Average Guiltiness 0.506 serving
Duration of Action 14 days
Effect Size very weakly negative
Number of Paired Measurements 137
Optimal Pearson Product 0.00035334728460952
P Value 0.37687
Statistical Significance 0.0482
Strength of Relationship 0.35871
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Dark Chocolate Nuts & Sea Salt Info

Property Value
Variable Name Dark Chocolate Nuts & Sea Salt
Aggregation Method SUM
Analysis Performed At 2020-09-17
Duration of Action 14 days
Filling Value 0
Kurtosis 47.590889712871
Maximum Allowed Value 40 serving
Mean 0.80631378571429 serving
Median 0 serving
Minimum Allowed Value 0 serving
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 34
Number of Measurements 7
Number of Measurements (including those generated by tagged, joined, or child variables) 7
Public true
Onset Delay 30 minutes
Standard Deviation 0.26381321306329
Unit Serving
User Variables 4
UPC 0
Variable Category Foods
Variable ID 1777
Variance 0.069597469693015

Guiltiness Info

Property Value
Variable Name Guiltiness
Aggregation Method MEAN
Analysis Performed At 2022-09-29
Duration of Action 24 hours
Kurtosis 2.2615389805518
Maximum Allowed Value 5 out of 5
Mean 2.365934400949 out of 5
Median 2.2960569395018 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 2056
Number of Aggregate Outcomes 263
Number of Measurements 31621
Number of Measurements (including those generated by tagged, joined, or child variables) 31621
Public true
Onset Delay 0 seconds
Standard Deviation 0.5743531754414
Unit 1 to 5 Rating
User Variables 1787
UPC 0
Variable Category Emotions
Variable ID 1335
Variance 0.72121328198661

Introduction

Background

Dark Chocolate Nuts & Sea Salt (Foods) and Guiltiness (Emotions) are both important factors in understanding human health and well-being. This study investigates the relationship between these two variables using real-world observational data.

Traditional randomized controlled trials (RCTs), while the gold standard for causal inference, are often impractical, expensive, or unethical for studying many health relationships. Aggregated N-of-1 observational studies offer a complementary approach that leverages within-subject comparisons across large populations to identify meaningful patterns.

Research Question

Does Dark Chocolate Nuts & Sea Salt affect Guiltiness?

Additionally, we seek to determine:

  1. What is the direction and magnitude of any effect?
  2. How confident can we be in this relationship based on the available data?
  3. What are the optimal levels of Dark Chocolate Nuts & Sea Salt for maximizing Guiltiness?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Dark Chocolate Nuts & Sea Salt and Guiltiness. Additionally, we attempt to determine the Dark Chocolate Nuts & Sea Salt values most likely to produce optimal Guiltiness values.

Study Overview

This is a population-level observational study using aggregated N-of-1 methodology. By aggregating individual N-of-1 experiments, we can identify population-level patterns while accounting for the substantial individual variation that exists in most health relationships. Effect sizes are reported as percent change from baseline, enabling intuitive interpretation and comparison across different measures.

Full Methodology: Framework for Real-World Evidence-Based Pharmacovigilance: Aggregated N-of-1 Trials for Quantifying Treatment Effects

Discussion

Interpretation of Findings

Participants experienced a 3.3% reduction in Guiltiness following above-average Dark Chocolate Nuts & Sea Salt exposure. The Predictor Impact Score (PIS) of 0.19 indicates weak evidence for a causal relationship. This finding is statistically significant (p = 0.048).

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Guiltiness is not statistically significant at a 95% confidence interval. This suggests that the Dark Chocolate Nuts & Sea Salt value may not have a significant influence on the Guiltiness value, or that more data is needed to detect an effect.

After treatment, a 0.6% increase (-0.0736 out of 5) from the mean baseline 2.22 out of 5 was observed. The relative standard deviation at baseline was 46.9%. The observed change was 0.070465 times the standard deviation.

A common rule of thumb considers a change greater than twice the baseline standard deviation on two separate pre-post experiments may be considered significant. This occurrence would have only a 5% likelihood of resulting from random fluctuation (a p-value < 0.05).

T-Test Details
Observed t-value: 0.337
Critical t-value: 1.646

Since t = 0.34 < 1.65, we cannot reject the null hypothesis.

Biological Plausibility

A plausible bio-chemical mechanism between predictor and outcome is critical for interpreting observational findings. This is where human judgment excels beyond statistical analysis.

Based on community responses so far, 1 person feels that there is a plausible mechanism of action and 0 feel that any relationship observed between Dark Chocolate Nuts & Sea Salt and Guiltiness is coincidental.

Bradford Hill Criteria Assessment

The Bradford Hill criteria provide a framework for assessing causality in observational studies. Our methodology operationalizes six of the nine criteria through the Predictor Impact Score (PIS):

Criterion How Addressed Metric
Strength Effect size magnitude Percent change from baseline (Δ%), z-score
Consistency Cross-participant replication Number of users (N), number of pairs (n)
Temporality Predictor precedes outcome Temporality factor (φ), onset delay (δ > 0)
Biological Gradient Dose-response relationship Gradient coefficient (φgradient)
Plausibility Biological mechanism assessment Community votes on mechanism plausibility
Specificity Category appropriateness Interest factor (finterest)

Predictor Impact Score (PIS)

The PIS integrates multiple Bradford Hill criteria into a composite metric quantifying how reliably a predictor affects an outcome. Higher scores indicate stronger evidence:

Population-Level PIS:

$$\text{PIS}_{\text{agg}} = |r_{\text{forward}}| \cdot w \cdot \phi_{\text{users}} \cdot \phi_{\text{pairs}} \cdot \phi_{\text{change}} \cdot \phi_{\text{gradient}}$$

Where φ-factors are saturation functions approaching 1 as evidence accumulates:

  • φusers = 1 - e-N/10 (user saturation)
  • φpairs = 1 - e-n/nsig (pair saturation)
  • φchange = 1 - e-Δspread/Δsig (effect spread saturation)
  • w = weighted average of plausibility votes

Temporality Assessment

We assess evidence for correct causal direction using the temporality factor:

$$\phi_{\text{temporal}} = \frac{|r_{\text{forward}}|}{|r_{\text{forward}}| + |r_{\text{reverse}}|}$$

Values approaching 1 indicate the predictor precedes the outcome (supporting causation); values near 0.5 suggest ambiguous directionality; values near 0 suggest reverse causation or confounding by indication.

Limitations

As with any observational study, correlation does not prove causation. Key limitations include:

  • Unmeasured confounders: Variables not tracked may influence results
  • Self-selection bias: Health trackers may differ from the general population
  • Measurement error: Self-reported data may contain recall bias
  • Confounding by indication: Sicker individuals may use more treatments

However, within-subject comparison and temporal precedence analysis partially mitigate these limitations. If the relationship is merely coincidental, as participants independently modify their Dark Chocolate Nuts & Sea Salt values, the observed strength will decline over time. Spurious correlations naturally dissipate as more data is collected.

Future Directions

Future research should examine:

  • Subgroup analyses to identify individual differences in response
  • Potential confounders and mediators of the observed relationship
  • Optimal dosing and timing for Dark Chocolate Nuts & Sea Salt
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 2 participants, these results are based on limited data. Effect sizes and confidence will improve as more participants contribute data. Consider these findings directional rather than definitive.

Above-average Dark Chocolate Nuts & Sea Salt was associated with a 3.3% reduction in Guiltiness—a minimal effect. The Predictor Impact Score of 0.19 indicates this relationship is warranting continued monitoring.

Bottom Line: Based on a PIS of 0.19 and a 3.3% effect size, this relationship shows weak evidence. Additional observational data is recommended before investing in experimental validation. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Dark Chocolate Nuts & Sea Salt may influence Guiltiness in real-world conditions. The within-subject design and temporal analysis provide confidence in these relationships, though observational limitations remain.

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Methods

Study Design

This study is based on data donated by 2 participants. Thus, the study design is equivalent to the aggregation of 2 separate n=1 observational natural experiments.

This within-subject design is powerful because it controls for all stable individual characteristics (genetics, baseline health status, socioeconomic factors) that might otherwise confound the relationship between variables.

Data Analysis

Temporal Assumptions

The analysis incorporates temporal assumptions about the relationship between variables:

  • Onset Delay: It was assumed that 30 minutes would pass before a change in Dark Chocolate Nuts & Sea Salt would produce an observable change in Guiltiness.
  • Duration of Action: It was assumed that Dark Chocolate Nuts & Sea Salt could produce an observable change in Guiltiness for as much as 14 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Dark Chocolate Nuts & Sea Salt values and subsequent Guiltiness values. Individual correlations were then aggregated using Fisher's z-transformation to produce a population-level estimate:

Individual Correlation:

$$r_i = \frac{\sum(x_{ij} - \bar{x}_i)(y_{ij} - \bar{y}_i)}{\sqrt{\sum(x_{ij} - \bar{x}_i)^2 \sum(y_{ij} - \bar{y}_i)^2}}$$

Fisher's Z-Transformation:

$$z_i = \frac{1}{2} \ln\left(\frac{1 + r_i}{1 - r_i}\right)$$

Aggregated Correlation:

$$\bar{r} = \tanh(\bar{z}) \quad \text{where} \quad \bar{z} = \frac{1}{N}\sum_{i=1}^{N} z_i$$

Effect Size Calculation

Effect sizes are reported as percent change from baseline. For each participant, we compare the outcome following above-average predictor values to the overall baseline outcome:

$$\Delta\%_{\text{baseline}} = \frac{\bar{O}_{\text{follow-up}} - \bar{O}_{\text{baseline}}}{\bar{O}_{\text{baseline}}} \times 100$$

Effect Magnitude (Z-Score)

To assess effect magnitude relative to natural variability, we calculate the z-score:

$$z = \frac{|\Delta\%_{\text{baseline}}|}{\text{RSD}_{\text{baseline}}}$$

where RSDbaseline is the relative standard deviation of outcome during baseline period

A z-score > 2 indicates statistical significance (p < 0.05), meaning the observed change exceeds typical baseline fluctuation and is unlikely due to random variation.

Statistical Significance

Correlation significance is assessed using a two-tailed t-test:

$$t = \frac{r\sqrt{n-2}}{\sqrt{1-r^2}}$$

We reject the null hypothesis (ρ = 0) at α = 0.05 when |t| exceeds the critical value, providing statistical evidence that the observed relationship is not due to chance.

Data Sources

Dark Chocolate Nuts & Sea Salt data was primarily collected using MyFitnessPal. Lose weight with MyFitnessPal, the fastest and easiest-to-use calorie counter for iPhone and iPad. With the largest food database of any iOS calorie counter (over 3,000,000 foods), and amazingly fast food and exercise entry.

Guiltiness data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Data Quality

Data quality measures were applied to ensure reliable results:

  • Minimum Data Requirement: Only participants with sufficient paired observations were included in the analysis.
  • Outlier Handling: Extreme values were winsorized to reduce the influence of measurement errors.
  • Missing Data: Days with missing values were handled using appropriate filling strategies based on the variable type.
  • Test User Exclusion: Test accounts and invalid users were excluded from all analyses.

Principal Investigator

Program & Methods

Mike P. Sinn
Sinn, M. P.

Designed and implemented data collection, aggregation, causal inference pipeline, and automated study generation framework. Developed the Predictor Impact Score methodology operationalizing Bradford Hill criteria for ranking causal relationships in observational data. When he tells people this at parties, they usually say they have to go check on their car.

Individual study outputs are automated, reproducible, and open to external audit. (Which I would seriously recommend.)

Contact

Cite This Study

APA Format
Sinn, M. P. (2026). Causal Analysis: Does Dark Chocolate Nuts & Sea Salt Affect Guiltiness?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1777-effect-1335-population-study
BibTeX 
@misc{sinn_cause_1777_effect_1335_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Dark Chocolate Nuts & Sea Salt Affect Guiltiness?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1777-effect-1335-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Dark Chocolate Nuts & Sea Salt Affect Guiltiness?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-1777-effect-1335-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Dark Chocolate Nuts & Sea Salt Affect Guiltiness?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1777-effect-1335-population-study [Accessed January 9, 2026].

Study Type: Aggregated N-of-1 Observational Mega-Study
Evidence Level: Level II (Real-World Evidence)
Methodology: Bradford Hill Criteria with Predictor Impact Score (PIS)

References

This framework was originally developed in 2013 based on the Bradford Hill criteria. Subsequent literature has independently validated similar approaches to causal inference from observational data:

  1. Hill, A.B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295-300. [Bradford Hill criteria]
  2. Lillie, E.O., et al. (2011). The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized Medicine, 8(2), 161-173. [N-of-1 methodology]
  3. Pearl, J. (2009). Causality: Models, Reasoning, and Inference . Cambridge University Press. [Causal inference]
  4. Hernán, M.A., & Robins, J.M. (2020). Causal Inference: What If . Chapman & Hall/CRC. [Free textbook]
  5. FDA (2018). Framework for FDA's Real-World Evidence Program . U.S. Food and Drug Administration. [Regulatory context]
  6. Duan, N., et al. (2013). Single-patient (n-of-1) trials: a pragmatic clinical decision methodology . Journal of Clinical Epidemiology, 66(8), S21-S28.
  7. Platt, R., et al. (2018). The FDA Sentinel Initiative—an evolving national resource . New England Journal of Medicine, 379(22), 2091-2093.

This information is for research and educational purposes only, not medical advice. Consult a healthcare provider before making health decisions. Terms of Service

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