Higher Facebook Pages Liked Predicts Slightly Lower Code Commits for Population

Higher Facebook Pages Liked Predicts Slightly Lower Code Commits for Population

Contents

Variables

A
Facebook Pages Liked 916
A
Code Commits 1808

Categories

A
Social Interactions 78
A
Goals 126

Tags

High Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 89.9% average decrease in Code Commits following above average Facebook Pages Liked.

Abstract

Code Commits was generally 9% higher than average after a total of 0.167 event of Facebook Pages Liked over the previous 7 days.

Aggregated data from 7 study participants suggests with a HIGH degree of confidence (p=0.132, 95% CI -1.127 to 0.809) that Facebook Pages Liked has a weakly negative predictive relationship (R=-0.159) with Code Commits.

The highest quartile of Code Commits measurements were observed following an average 0.12 event Facebook Pages Liked per day.

The lowest quartile of Code Commits measurements were observed following an average 0.235 event of Facebook Pages Liked per day.

After an onset delay of 0 seconds, Code Commits is typically 23% lower than average over the 7 days following around 0.235 event of Facebook Pages Liked Facebook Pages Liked.

Keywords: Facebook Pages Liked, Code Commits, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 7 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 802 paired observations from 7 participants revealed a substantial reduction in Code Commits following above-average Facebook Pages Liked exposure.

-65.8%
Change from Baseline
Substantial effect on Code Commits
0.04
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
-0.159
Correlation (r)
p = 0.432
Significance
z = 0.20
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Facebook Pages Liked:

  • Code Commits decreased by 65.8% on average
  • Temporal analysis supports Facebook Pages Liked as the predictor (not the outcome)

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 7 participants, these scores are preliminary and will become more reliable as additional data is collected.

Optimal Daily Values (Precision Dosing)

Based on the observed relationship, we can estimate the predictor values associated with the best and worst outcomes. These values enable personalized dosing recommendations.

⚠️ Preliminary Data: With 7 participants and 802 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

0.2 event
Value Predicting Higher Code Commits
Average Facebook Pages Liked when Code Commits exceeded its mean
0.8 event
Value Predicting Lower Code Commits
Average Facebook Pages Liked when Code Commits was below its mean

What This Suggests

Code Commits tended to be lowest (best) when Facebook Pages Liked was around 0.8 event.

Important: These values reflect correlations, not guaranteed causal effects. Individual responses may vary. Use as a starting point for personal experimentation, not as a definitive prescription. Consult healthcare providers before making treatment decisions.

Population Correlation

Facebook Pages Liked Distribution

Code Commits Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Facebook Pages Liked
Effect Variable Name Code Commits
Sinn Predictive Coefficient 0.039945957206359
Confidence Level HIGH
Confidence Interval 0.96816
Forward Pearson Predictive Coefficient -0.1587
Critical T Value 1.6483
Total Facebook Pages Liked Over Previous 7 days Before ABOVE Average Code Commits 0.12 event
Total Facebook Pages Liked Over Previous 7 days Before BELOW Average Code Commits 0.235 event
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 802
Optimal Pearson Product 0.032988819606433
P Value 0.13245
Statistical Significance 0.4319
Strength of Relationship 0.96816
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 7

Facebook Pages Liked Info

Property Value
Variable Name Facebook Pages Liked
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 7 days
Filling Value 0
Kurtosis 273.83865391441
Mean 0.27514379698492 event
Median 0.020100502512563 event
Minimum Allowed Value 0 event
Number of Aggregate Predictors 732
Number of Aggregate Outcomes 184
Number of Measurements 164257
Number of Measurements (including those generated by tagged, joined, or child variables) 151770
Public true
Onset Delay 0 seconds
Standard Deviation 1.0850111721454
Unit Event
User Variables 461
UPC 0
Variable Category Social Interactions
Variable ID 5969791
Variance 3.5942247713855

Code Commits Info

Property Value
Variable Name Code Commits
Aggregation Method SUM
Analysis Performed At 2021-06-17
Duration of Action 7 days
Filling Value 0
Kurtosis 248.43655329426
Mean 1.0091009148936 event
Median 0.38297872340426 event
Minimum Allowed Value 0 event
Number of Aggregate Predictors 1680
Number of Aggregate Outcomes 128
Number of Measurements 103731
Number of Measurements (including those generated by tagged, joined, or child variables) 103731
Public true
Onset Delay 0 seconds
Standard Deviation 2.6025070693278
Unit Event
User Variables 51
Variable Category Goals
Variable ID 5955693
Variance 54.388599127586

Introduction

Background

Facebook Pages Liked (Social Interactions) and Code Commits (Goals) 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 Facebook Pages Liked affect Code Commits?

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 Facebook Pages Liked for maximizing Code Commits?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Facebook Pages Liked and Code Commits. Additionally, we attempt to determine the Facebook Pages Liked values most likely to produce optimal Code Commits 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 65.8% reduction in Code Commits following above-average Facebook Pages Liked exposure. The Predictor Impact Score (PIS) of 0.04 indicates insufficient evidence for a causal relationship.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Code Commits is statistically significant at a 95% confidence interval. The p-value of 0.4319 indicates there is less than a 43.19% probability that this result occurred by chance.

After treatment, a 89.9% decrease (-7.59 event) from the mean baseline 9.42 event was observed. The relative standard deviation at baseline was 509.9%. The observed change was 0.19799 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: 4.184
Critical t-value: 1.648

Since t = 4.18 > 1.65, we 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.

Community feedback on the biological plausibility of this relationship is still being collected. Consider the known mechanisms by which Facebook Pages Liked might influence Code Commits.

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 Facebook Pages Liked 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 Facebook Pages Liked
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 7 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 Facebook Pages Liked was associated with a 65.8% reduction in Code Commits—a substantial effect. The Predictor Impact Score of 0.04 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.04 and a 65.8% effect size, this relationship currently lacks sufficient evidence. Continue monitoring as more data becomes available. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Facebook Pages Liked may influence Code Commits in real-world conditions. While preliminary, these results may inform future research directions. As more participants contribute data, the reliability and precision of these findings will improve substantially.

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Methods

Study Design

This study is based on data donated by 7 participants. Thus, the study design is equivalent to the aggregation of 7 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 0 seconds would pass before a change in Facebook Pages Liked would produce an observable change in Code Commits.
  • Duration of Action: It was assumed that Facebook Pages Liked could produce an observable change in Code Commits for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Facebook Pages Liked values and subsequent Code Commits 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

Facebook Pages Liked 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.

Code Commits 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 Facebook Pages Liked Affect Code Commits?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5969791-effect-5955693-population-study
BibTeX 
@misc{sinn_cause_5969791_effect_5955693_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Facebook Pages Liked Affect Code Commits?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5969791-effect-5955693-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Facebook Pages Liked Affect Code Commits?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-5969791-effect-5955693-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Facebook Pages Liked Affect Code Commits?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5969791-effect-5955693-population-study [Accessed January 10, 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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