**V**and experimental impact

**c**can act as a guide. Virality is when one action triggers another, such as sending a message triggers the next person to send a message. Let

**p < 1**be the probability an action triggers another. Then the virality influence

**V**is the expected total number of messages sent. In general for such actions as likes, comments, and shares

**V < 0.2**. In short here are the variables:

Cohort | Actual Performance | Observed Performance |
---|---|---|

A | x | z |

B | a · x | c · z |

By assuming all interference is across cohorts, we can bound the actual experimental impact **a**. The relation between observed to the actual performance is then:

With a small bit of algebra we find a beautiful bound:

So, why is it gorgeous? Let us consider the extrema.

- In the ideal experiment
**V = 0**and the bound is**a = c**. Nice! Our bound is tight. - When all activity in cohort
**B**is a byproduct of cohort**A**,**c = V**and the bound is**a = 0**. So in a dangerous system,**a**becomes scary.

In general the bound remains tight for low values of V and explodes with increasing social influence. In social networks, there are hundreds of features with **V > 0**. Fortunately, it’s easy to bound how much virality is impacting your results. If the impact is larger than a tolerable amount of error, then time to bring out the sledge hammer and split up your network. If it’s a tolerable amount of error, then march forward and conquer with your traditional a/b framework! ** When in doubt. Gold:**

. . . . Additional Examples with **V = 0.04**:

Cohort | Actual Performance (T) | Observed Performance (T) | Actual Diff Bound |
---|---|---|---|

A | x | z | |

B | a x | 0.97 · z | [-3.2%, -3%] |

B’ | a’ x | 1.03 · z | [+3%, +3.3%] |