For most, Facebook Likes is generally highest after an average of 2000 meters of Walk Or Run Distance over the previous 7 days.


Abstract
Aggregated data from 63 study participants suggests with a high degree of confidence (p=0.20292557726942, 95% CI 1.591 to 1.55) that Walk Or Run Distance has a very weakly negative predictive relationship (R=0.02) with Facebook Likes. The highest quartile of Facebook Likes measurements were observed following an average 23 meters Walk Or Run Distance. The lowest quartile of Facebook Likes measurements were observed following an average 22269.003711945 m Walk Or Run Distance.
Objective
The objective of this study is to determine the nature of the relationship (if any) between Walk Or Run Distance and Facebook Likes. Additionally, we attempt to determine the Walk Or Run Distance values most likely to produce optimal Facebook Likes values.
Participant Instructions
Get Fitbit here and use it to record your Walk Or Run Distance. Once you have a Fitbit account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.
Record your Facebook Likes daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Facebook Likes daily in the reminder inbox or using the interactive web or mobile notifications.
Design
This study is based on data donated by 63 participants. Thus, the study design is equivalent to the aggregation of 63 separate n=1 observational natural experiments.
Data Analysis
Walk Or Run Distance PreProcessing
Walk Or Run Distance measurement values below 0 meters were assumed erroneous and removed. No maximum allowed measurement value was defined for Walk Or Run Distance. No missing data filling value was defined for Walk Or Run Distance so any gaps in data were just not analyzed instead of assuming zero values for those times.
Facebook Likes PreProcessing
Facebook Likes measurement values below 0 event were assumed erroneous and removed. No maximum allowed measurement value was defined for Facebook Likes. It was assumed that any gaps in Facebook Likes data were unrecorded 0 event measurement values.
Predictive Analytics
It was assumed that 0 hours would pass before a change in Walk Or Run Distance would produce an observable change in Facebook Likes. It was assumed that Walk Or Run Distance could produce an observable change in Facebook Likes for as much as 7 days after the stimulus event.
Walk Or Run Distance measurement values below 0 meters were assumed erroneous and removed. No maximum allowed measurement value was defined for Walk Or Run Distance. No missing data filling value was defined for Walk Or Run Distance so any gaps in data were just not analyzed instead of assuming zero values for those times.

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Walk Or Run Distance would produce an observable change in Facebook Likes. It was assumed that Walk Or Run Distance could produce an observable change in Facebook Likes for as much as 7 days after the stimulus event.

Data Sources
Walk Or Run Distance data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.
Facebook Likes 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 Likes 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 correlation. 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 Walk Or Run Distance and Facebook Likes
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. 582 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Walk Or Run Distance 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 Walk Or Run Distance and Facebook Likes 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 Walk Or Run Distance and Facebook Likes
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. 582 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Walk Or Run Distance 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 Walk Or Run Distance and Facebook Likes 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  Walk Or Run Distance 
Effect Variable Name  Facebook Likes 
Sinn Predictive Coefficient  0.020219040605482 
Confidence Level  high 
Confidence Interval  1.570501806351 
Forward Pearson Correlation Coefficient  0.0203 
Critical T Value  1.6613015875831 
Average Walk Or Run Distance Over Previous 7 days Before ABOVE Average Facebook Likes  23 meters 
Average Walk Or Run Distance Over Previous 7 days Before BELOW Average Facebook Likes  22269.003711945 meters 
Duration of Action  7 days 
Effect Size  very weakly negative 
Number of Paired Measurements  582 
Optimal Pearson Product  0.036475620081058 
P Value  0.20292557726942 
Statistical Significance  0.7374031714622 
Strength of Relationship  1.570501806351 
Study Type  population 
Analysis Performed At  20190129 
Number of Participants  63 
Walk Or Run Distance Statistics
Property  Value 

Variable Name  Walk Or Run Distance 
Aggregation Method  MEAN 
Analysis Performed At  20190127 
Duration of Action  7 days 
Kurtosis  7.2815955969397 
Mean  13029.585585284 meters 
Median  12130.745434836 meters 
Minimum Allowed Value  0 meters 
Number of Correlations  774 
Number of Measurements  2294898 
Onset Delay  0 seconds 
Standard Deviation  6268.2441881728 
Unit  Meters 
UPC  744960759935 
Variable ID  1304 
Variance  74161818.911518 
Facebook Likes Statistics
Property  Value 

Variable Name  Facebook Likes 
Aggregation Method  SUM 
Analysis Performed At  20190127 
Duration of Action  7 days 
Kurtosis  395.65850570769 
Mean  0.24544465928854 event 
Median  0.011857707509881 event 
Minimum Allowed Value  0 event 
Number of Correlations  573 
Number of Measurements  141294 
Onset Delay  0 seconds 
Standard Deviation  1.1730255645332 
Unit  Event 
Variable ID  1883 
Variance  3.8345376526322 
Principal Investigator  Mike Sinn