For most, Facebook Posts is generally highest after an average of 1.5 out of 5 of Irritability over the previous 24 hours.


Abstract
Aggregated data from 25 study participants suggests with a medium degree of confidence (p=0.20455417265931, 95% CI 2.127 to 1.977) that Irritability has a very weakly negative predictive relationship (R=0.07) with Facebook Posts. The highest quartile of Facebook Posts measurements were observed following an average 1.94 out of 5 Irritability. The lowest quartile of Facebook Posts measurements were observed following an average 2.4909472511946 /5 Irritability.
Objective
The objective of this study is to determine the nature of the relationship (if any) between Irritability and Facebook Posts. Additionally, we attempt to determine the Irritability values most likely to produce optimal Facebook Posts values.
Participant Instructions
Record your Irritability daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Facebook Posts daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Facebook Posts daily in the reminder inbox or using the interactive web or mobile notifications.
Design
This study is based on data donated by 25 participants. Thus, the study design is equivalent to the aggregation of 25 separate n=1 observational natural experiments.
Data Analysis
Irritability PreProcessing
Irritability measurement values below 1 out of 5 were assumed erroneous and removed. Irritability measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Irritability so any gaps in data were just not analyzed instead of assuming zero values for those times.
Facebook Posts PreProcessing
Facebook Posts measurement values below 0 event were assumed erroneous and removed. No maximum allowed measurement value was defined for Facebook Posts. It was assumed that any gaps in Facebook Posts data were unrecorded 0 event measurement values.
Predictive Analytics
It was assumed that 0 hours would pass before a change in Irritability would produce an observable change in Facebook Posts. It was assumed that Irritability could produce an observable change in Facebook Posts for as much as 1 days after the stimulus event.
Irritability measurement values below 1 out of 5 were assumed erroneous and removed. Irritability measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Irritability so any gaps in data were just not analyzed instead of assuming zero values for those times.

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Irritability would produce an observable change in Facebook Posts. It was assumed that Irritability could produce an observable change in Facebook Posts for as much as 1 days after the stimulus event.

Data Sources
Irritability 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.
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.
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.
Limitations
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 nonexistent 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.
The list of the criteria is as follows:
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 very weakly negative relationship between Irritability and Facebook Posts
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. 62 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Irritability 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 timeprecedence 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 biochemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 1 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Irritability and Facebook Posts 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.
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.
The list of the criteria is as follows:
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 very weakly negative relationship between Irritability and Facebook Posts
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. 62 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Irritability 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 timeprecedence 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 biochemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 1 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Irritability and Facebook Posts 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.
Relationship Statistics
Property  Value 

Cause Variable Name  Irritability 
Effect Variable Name  Facebook Posts 
Sinn Predictive Coefficient  0.031681180634052 
Confidence Level  medium 
Confidence Interval  2.0518169143187 
Forward Pearson Correlation Coefficient  0.0747 
Critical T Value  1.7264079971008 
Average Irritability Over Previous 24 hours Before ABOVE Average Facebook Posts  1.94 out of 5 
Average Irritability Over Previous 24 hours Before BELOW Average Facebook Posts  2.4909472511946 out of 5 
Duration of Action  24 hours 
Effect Size  very weakly negative 
Number of Paired Measurements  62 
Optimal Pearson Product  0.16743099331441 
P Value  0.20455417265931 
Statistical Significance  0.25539200037718 
Strength of Relationship  2.0518169143187 
Study Type  population 
Analysis Performed At  20190129 
Number of Participants  25 
Irritability Statistics
Property  Value 

Variable Name  Irritability 
Aggregation Method  MEAN 
Analysis Performed At  20190120 
Duration of Action  24 hours 
Kurtosis  4.4960400745927 
Maximum Allowed Value  5 out of 5 
Mean  2.5165796968056 out of 5 
Median  2.4672470652897 out of 5 
Minimum Allowed Value  1 out of 5 
Number of Correlations  462 
Number of Measurements  48412 
Onset Delay  0 seconds 
Standard Deviation  0.51216330843249 
Unit  1 to 5 Rating 
Variable ID  1358 
Variance  0.57553481012702 
Facebook Posts Statistics
Property  Value 

Variable Name  Facebook Posts 
Aggregation Method  SUM 
Analysis Performed At  20190127 
Duration of Action  7 days 
Kurtosis  85.903691814345 
Mean  0.51188199632653 event 
Median  0.1469387755102 event 
Minimum Allowed Value  0 event 
Number of Correlations  350 
Number of Measurements  260487 
Onset Delay  0 seconds 
Standard Deviation  1.064832628711 
Unit  Event 
Variable ID  1884 
Variance  2.4277407729949 
Principal Investigator  Mike Sinn