Higher Caffiene Predicts Significantly Lower Shame for Population
Contents

Variables

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Caffiene 10
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Shame 1006

Categories

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Causes of Illness 2289
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Emotions 2028

Actions

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Your Data

Tags

Low Confidence
Strong Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 26.3% average decrease in Shame following above average Caffiene.
Abstract

Abstract

No confidenceInterval for Aggregate Correlation:

No confidenceInterval after attempting to average from 1 user correlations.

No confidenceInterval after attempting to average from 1 user correlations.

Shame was generally 8.3% lower than average after 8 count of Caffiene per 7 days.

Aggregated data from 1 study participants suggests with a LOW degree of confidence (10 overlapping data points) that Caffiene has a strongly negative predictive relationship (R=-0.742) with Shame.

The highest quartile of Shame measurements were observed following an average 2.25 count Caffiene per day.

The lowest quartile of Shame measurements were observed following an average 7 count of Caffiene per day.

After an onset delay of 0 seconds, Shame is typically 21% lower than average over the 7 days following around 7 count Caffiene.

Objective

Objective

The objective of this study is to determine the nature of the relationship (if any) between Caffiene and Shame. Additionally, we attempt to determine the Caffiene values most likely to produce optimal Shame values.
Participant Instructions

Participant Instructions

Manual Recording Option

A Create a reminder for Caffiene here and record it daily by enabling notifications or using A the reminder inbox here .


Manual Recording Option

A Create a reminder for Shame here and record it daily by enabling notifications or using A the reminder inbox here .

Design

Design

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

Data Analysis

Data Analysis

Caffiene Pre-Processing

Caffiene measurement values below 0 count were assumed erroneous and removed. No maximum allowed measurement value was defined for Caffiene. It was assumed that any gaps in Caffiene data were unrecorded 0 count measurement values.

Shame Pre-Processing

Shame measurement values below 1 out of 5 were assumed erroneous and removed. Shame measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Shame so any gaps in data were just not analyzed instead of assuming zero values for those times.

Predictive Analytics

It was assumed that 0 seconds would pass before a change in Caffiene would produce an observable change in Shame.

It was assumed that Caffiene could produce an observable change in Shame for as much as 7 days after the stimulus event.

Statistical Significance

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Shame is not statistically significant at a 95% confidence interval. This suggests that the Caffiene value does not have a significant influence on the Shame value.

After treatment, a 26.3% decrease (-0.333 out of 5) from the mean baseline 4 out of 5 was observed. The relative standard deviation at baseline was 10.5%. The observed change was 0.79057 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).

Data Sources

Data Sources

Caffiene 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.

Shame 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.

Limitations

Limitations

The accuracy of this study may be limited by the fact that

No confidenceInterval for Aggregate Correlation:

No confidenceInterval after attempting to average from 1 user correlations.

No confidenceInterval after attempting to average from 1 user correlations. . A greater amount of data and more variance in the data would help to resolve this issue.

As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply causation. We can never know for sure if one factor is definitely the cause of an outcome. However, lack of correlation definitely implies the lack of a causal relationship. Hence, we can with great confidence rule out non-existent relationships. For instance, if we discover no relationship between mood and an antidepressant this information is just as or even more valuable than the discovery that there is a relationship.

We can also take advantage of several characteristics of time series data from many subjects to infer the likelihood of a causal relationship if we do find a correlational relationship. The criteria for causation are a group of minimal conditions necessary to provide adequate evidence of a causal relationship between an incidence and a possible consequence.

Criteria For Causal Inference

Strength (A.K.A. Effect Size)

A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal. There is a strongly negative (R = -0.742) relationship between Caffiene and Shame.

Consistency (A.K.A. Reproducibility)

Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 10 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Caffiene values, the observed strength of the relationship will decline until it is below the threshold of significance. To it another way, in the case that we do find a spurious correlation, suggesting that banana intake improves mood for instance, one will likely increase their banana intake. Due to the fact that this correlation is spurious, it is unlikely that you will see a continued and persistent corresponding increase in mood. So over time, the spurious correlation will naturally dissipate.

Specificity

Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.

Temporality

The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay). The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations.

Biological Gradient

Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.

Plausibility

A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel.

Based on our responses so far,

0 humans feel that there is a plausible mechanism of action for a relationship between Caffiene and Shame.

0 humans feel that any relationship observed between Caffiene and Shame is coincidental.

Coherence

Coherence between epidemiological and laboratory findings increases the likelihood of an effect. It will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.

Experiment

All of human life can be considered a natural experiment. Occasionally, it is possible to appeal to experimental evidence.

Analogy

The effect of similar factors may be considered.

Plausibility

Plausibility

A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 0 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Caffiene and Shame is coincidental.

Relationship Statistics

Property Value
Cause Variable Name Caffiene
Effect Variable Name Shame
Sinn Predictive Coefficient 0.070610634274498
Confidence Level LOW
Confidence Interval 0
Forward Pearson Predictive Coefficient -0.742
Critical T Value 0
Total Caffiene Over Previous 7 days Before ABOVE Average Shame 2.25 count
Total Caffiene Over Previous 7 days Before BELOW Average Shame 7 count
Duration of Action 7 days
Effect Size strongly negative
Number of Paired Measurements 10
Optimal Pearson Product 1.3054698612718
P Value 0
Statistical Significance 0.0023
Strength of Relationship 0
Study Type population
Analysis Performed At 2021-08-30
Number of Participants 1

Caffiene Info

Property Value
Variable Name Caffiene
Aggregation Method SUM
Analysis Performed At 2020-10-09
Duration of Action 7 days
Filling Value 0
Kurtosis 3.7469628011736
Mean 0.58182 count
Median 0 count
Minimum Allowed Value 0 count
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 10
Number of Measurements 17
Number of Measurements (including those generated by tagged, joined, or child variables) 17
Public true
Onset Delay 0 seconds
Standard Deviation 0.99425961828498
Unit Count
User Variables 1
UPC 181030000366
Variable Category Causes of Illness
Variable ID 96469
Variance 0.98855218855219

Shame Info

Property Value
Variable Name Shame
Aggregation Method MEAN
Analysis Performed At 2020-09-17
Duration of Action 24 hours
Kurtosis 3.5182882978692
Maximum Allowed Value 5 out of 5
Mean 2.2230770157837 out of 5
Median 2.1592856711705 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 890
Number of Aggregate Outcomes 116
Number of Measurements 20707
Number of Measurements (including those generated by tagged, joined, or child variables) 20608
Public true
Onset Delay 0 seconds
Standard Deviation 0.4756748348929
Unit 1 to 5 Rating
User Variables 1541
UPC 0
Variable Category Emotions
Variable ID 1443
Variance 0.54942944533929

Principal Investigator

Cite This Study

APA Format
Sinn, M. P. (2026). Higher Caffiene Predicts Significantly Lower Shame for Population. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-96469-effect-1443-population-study
BibTeX
@misc{sinn_cause_96469_effect_1443_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Higher Caffiene Predicts Significantly Lower Shame for Population},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-96469-effect-1443-population-study},
  note = {Accessed: January 3, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Higher Caffiene Predicts Significantly Lower Shame for Population." The Journal of Citizen Science. Accessed January 3, 2026. https://studies.crowdsourcingcures.org/study/cause-96469-effect-1443-population-study.