Higher Cloud Cover Amount Predicts Very Slightly Higher Facebook Posts for Population

Higher Cloud Cover Amount Predicts Very Slightly Higher Facebook Posts for Population

Contents

Variables

A
Cloud Cover Amount 231
A
Facebook Posts 909

Categories

A
Environment 564
A
Social Interactions 78

Actions

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Your Data

Tags

High Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 0.6% average decrease in Facebook Posts following above average Cloud Cover Amount.

Abstract

Facebook Posts was generally 8% higher than average after an average of 42.9 percent of Cloud Cover Amount over the previous 7 days.

Aggregated data from 69 study participants suggests with a HIGH degree of confidence (p=0.221, 95% CI -1.081 to 1.087) that Cloud Cover Amount has a very weakly positive predictive relationship (R=0.0027) with Facebook Posts.

The highest quartile of Facebook Posts measurements were observed following an average 43 percent Cloud Cover Amount.

The lowest quartile of Facebook Posts measurements were observed following an average 42.9 percent of Cloud Cover Amount.

After an onset delay of 0 seconds, Facebook Posts is typically 8% lower than average over the 7 days following around 42.9 percent of Cloud Cover Amount Cloud Cover Amount.

Keywords: Cloud Cover Amount, Facebook Posts, N-of-1 trials, real-world evidence, causal inference, observational study

High Confidence: With 69 participants, these findings have strong statistical power.

Results

Primary Findings

Analysis of 29,072 paired observations from 69 participants revealed a modest improvement in Facebook Posts following above-average Cloud Cover Amount exposure.

+5.7%
Change from Baseline
Modest effect on Facebook Posts
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.003
Correlation (r)
p = 0.947
Significance
z = 0.16
Effect Magnitude
Ļ† = 0.17
Temporality

What This Means

When participants had above-average Cloud Cover Amount:

  • Facebook Posts increased by 5.7% on average
  • Temporal analysis suggests possible reverse causationā€”interpret with caution

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
ā‰„ 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.

Optimal Daily Values

No clear dose-response relationship detected. The Cloud Cover Amount values associated with high and low Facebook Posts 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.

Population Correlation

Cloud Cover Amount Distribution

Facebook Posts Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Cloud Cover Amount
Effect Variable Name Facebook Posts
Sinn Predictive Coefficient 0.0013486395246532
Confidence Level HIGH
Confidence Interval 1.0841064445027
Forward Pearson Predictive Coefficient 0.0027
Critical T Value 1.6472028985507
Average Cloud Cover Amount Over Previous 7 days Before ABOVE Average Facebook Posts 43 percent
Average Cloud Cover Amount Over Previous 7 days Before BELOW Average Facebook Posts 42.9 percent
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 29072
Optimal Pearson Product 0.01550677089739
P Value 0.22055087804138
Statistical Significance 0.9466
Strength of Relationship 1.0841064445027
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 69

Cloud Cover Amount Info

Property Value
Variable Name Cloud Cover Amount
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 7 days
Kurtosis 2.0115150048528
Maximum Allowed Value 100 percent
Mean 44.949018299081 percent
Median 40.628521126761 percent
Minimum Allowed Value 0 percent
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 231
Number of Measurements 171797
Number of Measurements (including those generated by tagged, joined, or child variables) 1437
Public true
Onset Delay 0 seconds
Standard Deviation 35.25604949636
Unit Percent
User Variables 284
UPC 0
Variable Category Environment
Variable ID 5954747
Variance 1291.6193754063

Facebook Posts Info

Property Value
Variable Name Facebook Posts
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 7 days
Filling Value 0
Kurtosis 88.919441322101
Mean 0.51559340691057 event
Median 0.14634146341463 event
Minimum Allowed Value 0 event
Number of Aggregate Predictors 710
Number of Aggregate Outcomes 199
Number of Measurements 260487
Number of Measurements (including those generated by tagged, joined, or child variables) 247402
Public true
Onset Delay 0 seconds
Standard Deviation 1.0624567782965
Unit Event
User Variables 249
Variable Category Social Interactions
Variable ID 1884
Variance 2.4337113928859

Introduction

Background

Cloud Cover Amount (Environment) and Facebook Posts (Social Interactions) 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 Cloud Cover Amount affect Facebook Posts?

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 Cloud Cover Amount for maximizing Facebook Posts?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Cloud Cover Amount and Facebook Posts. Additionally, we attempt to determine the Cloud Cover Amount values most likely to produce optimal Facebook Posts 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 5.7% improvement in Facebook Posts following above-average Cloud Cover Amount exposure. The Predictor Impact Score (PIS) of 0.00 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 Facebook Posts is not statistically significant at a 95% confidence interval. This suggests that the Cloud Cover Amount value may not have a significant influence on the Facebook Posts value, or that more data is needed to detect an effect.

After treatment, a 0.6% decrease (0.0844 event) from the mean baseline 5.15 event was observed. The relative standard deviation at baseline was 147.304%. The observed change was 0.155923 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: 1.365
Critical t-value: 1.647

Since t = 1.37 < 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.

Community feedback on the biological plausibility of this relationship is still being collected. Consider the known mechanisms by which Cloud Cover Amount might influence Facebook Posts.

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 (Ī”%), z-score
Consistency Cross-participant replication Number of users (N), number of pairs (n)
Temporality Predictor precedes outcome Temporality factor (Ļ†), onset delay (Ī“ > 0)
Biological Gradient Dose-response relationship Gradient coefficient (Ļ†gradient)
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 Ļ†-factors are saturation functions approaching 1 as evidence accumulates:

  • Ļ†users = 1 - e-N/10 (user saturation)
  • Ļ†pairs = 1 - e-n/nsig (pair saturation)
  • Ļ†change = 1 - e-Ī”spread/Ī”sig (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 Cloud Cover Amount 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 Cloud Cover Amount
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

Above-average Cloud Cover Amount was associated with a 5.7% improvement in Facebook Postsā€”a modest effect. The Predictor Impact Score of 0.00 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.00 and a 5.7% effect size, this relationship currently lacks sufficient evidence. Continue monitoring as more data becomes available.

These findings contribute to our understanding of how Cloud Cover Amount may influence Facebook Posts in real-world conditions. While preliminary, these results may inform future research directions.

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Methods

Study Design

This study is based on data donated by 69 participants. Thus, the study design is equivalent to the aggregation of 69 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 Cloud Cover Amount would produce an observable change in Facebook Posts.
  • Duration of Action: It was assumed that Cloud Cover Amount could produce an observable change in Facebook Posts for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Cloud Cover Amount values and subsequent Facebook Posts 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 (Ļ = 0) at Ī± = 0.05 when |t| exceeds the critical value, providing statistical evidence that the observed relationship is not due to chance.

Data Sources

Cloud Cover Amount data was primarily collected using Weather. Automatically import temperature, humidity, and ultraviolet light exposure.

Facebook Posts 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 Cloud Cover Amount Affect Facebook Posts?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5954747-effect-1884-population-study
BibTeX 
@misc{sinn_cause_5954747_effect_1884_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Cloud Cover Amount Affect Facebook Posts?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5954747-effect-1884-population-study},
  note = {Accessed: January 6, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Cloud Cover Amount Affect Facebook Posts?." The Journal of Citizen Science. Accessed January 6, 2026. https://studies.crowdsourcingcures.org/study/cause-5954747-effect-1884-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Cloud Cover Amount Affect Facebook Posts?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5954747-effect-1884-population-study [Accessed January 6, 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. HernƔn, 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 Initiativeā€”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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