Higher Outdoor Humidity Predicts Very Slightly Higher Guiltiness for Population

Higher Outdoor Humidity Predicts Very Slightly Higher Guiltiness for Population

Contents

Variables

A
Outdoor Humidity 493
A
Guiltiness 2319

Categories

A
Environment 564
A
Emotions 2028

Actions

A
Join Study
A
Your Data

Tags

High Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 4% average increase in Guiltiness following above average Outdoor Humidity.

Abstract

Guiltiness was generally 5.05393% higher than average after 66.3 percent of Outdoor Humidity per 7 days.

Aggregated data from 89 study participants suggests with a HIGH degree of confidence (p=0.198, 95% CI -0.354 to 0.436) that Outdoor Humidity has a very weakly positive predictive relationship (R=0.0409) with Guiltiness.

The highest quartile of Guiltiness measurements were observed following an average 67.5 percent Outdoor Humidity.

The lowest quartile of Guiltiness measurements were observed following an average 66.9 percent of Outdoor Humidity.

After an onset delay of 0 seconds, Guiltiness is typically 5% lower than average over the 7 days following around 66.9 percent of Outdoor Humidity Outdoor Humidity.

Keywords: Outdoor Humidity, Guiltiness, N-of-1 trials, real-world evidence, causal inference, observational study

High Confidence: With 89 participants, these findings have strong statistical power.

Results

Primary Findings

Analysis of 6,233 paired observations from 89 participants revealed a modest improvement in Guiltiness following above-average Outdoor Humidity exposure.

+5.1%
Change from Baseline
Modest effect on Guiltiness
0.04
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.041
Correlation (r)
p = 0.284
Significance
z = 0.54
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Outdoor Humidity:

  • Guiltiness increased by 5.1% on average
  • Temporal analysis supports Outdoor Humidity 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.

Optimal Daily Values

No clear dose-response relationship detected. The Outdoor Humidity 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.

Population Correlation

Outdoor Humidity Distribution

Guiltiness Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Outdoor Humidity
Effect Variable Name Guiltiness
Sinn Predictive Coefficient 0.0408944211327
Confidence Level HIGH
Confidence Interval 0.39480273635018
Forward Pearson Predictive Coefficient 0.0409
Critical T Value 1.6790674157303
Average Outdoor Humidity Over Previous 7 days Before ABOVE Average Guiltiness 67.5 percent
Average Outdoor Humidity Over Previous 7 days Before BELOW Average Guiltiness 66.9 percent
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 6233
Optimal Pearson Product 0.11441649402879
P Value 0.1979544195183
Statistical Significance 0.2844
Strength of Relationship 0.39480273635018
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 89

Outdoor Humidity Info

Property Value
Variable Name Outdoor Humidity
Aggregation Method MEAN
Analysis Performed At 2020-09-11
Duration of Action 7 days
Kurtosis 2.6283326449295
Maximum Allowed Value 100 percent
Mean 68.41210747054 percent
Median 68.800418743769 percent
Minimum Allowed Value 0 percent
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 493
Number of Measurements 406399
Number of Measurements (including those generated by tagged, joined, or child variables) 114789
Public true
Onset Delay 0 seconds
Standard Deviation 11.29569799371
Unit Percent
User Variables 1037
UPC 0
Variable Category Environment
Variable ID 5954744
Variance 192.13369708306

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

Outdoor Humidity (Environment) 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 Outdoor Humidity 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 Outdoor Humidity for maximizing Guiltiness?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Outdoor Humidity and Guiltiness. Additionally, we attempt to determine the Outdoor Humidity 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 5.1% improvement in Guiltiness following above-average Outdoor Humidity 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 Guiltiness is not statistically significant at a 95% confidence interval. This suggests that the Outdoor Humidity value may not have a significant influence on the Guiltiness value, or that more data is needed to detect an effect.

After treatment, a 4% increase (0.0772 out of 5) from the mean baseline 2.36 out of 5 was observed. The relative standard deviation at baseline was 29.518%. The observed change was 0.542125 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: 1.528
Critical t-value: 1.679

Since t = 1.53 < 1.68, 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 Outdoor Humidity might influence Guiltiness.

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 Outdoor Humidity 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 Outdoor Humidity
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

Above-average Outdoor Humidity was associated with a 5.1% improvement in Guiltiness—a modest 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 5.1% effect size, this relationship currently lacks sufficient evidence. Continue monitoring as more data becomes available.

These findings contribute to our understanding of how Outdoor Humidity may influence Guiltiness in real-world conditions. While preliminary, these results may inform future research directions.

Help End Unnecessary Suffering

Current clinical trials are 82x more expensive than necessary and take 17 years to bring treatments to market. Pragmatic trials integrated into standard healthcare could reduce costs from $41,000 to $500 per participant and compress timelines to just 2 years. Learn how redirecting just 1% of global military spending could accelerate cures for the 2 billion people suffering from treatable diseases.

Make Suffering Optional

Methods

Study Design

This study is based on data donated by 89 participants. Thus, the study design is equivalent to the aggregation of 89 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 Outdoor Humidity would produce an observable change in Guiltiness.
  • Duration of Action: It was assumed that Outdoor Humidity could produce an observable change in Guiltiness for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Outdoor Humidity 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

Outdoor Humidity data was primarily collected using Weather. Automatically import temperature, humidity, and ultraviolet light exposure.

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 Outdoor Humidity Affect Guiltiness?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5954744-effect-1335-population-study
BibTeX 
@misc{sinn_cause_5954744_effect_1335_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Outdoor Humidity Affect Guiltiness?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5954744-effect-1335-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Outdoor Humidity Affect Guiltiness?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-5954744-effect-1335-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Outdoor Humidity Affect Guiltiness?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5954744-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

LinkedIn Reddit