Start with conversion
Compare follows per 1,000 impressions before interpreting raw growth. This separates being seen from converting attention.
A neutral comparison, not a verdict
Compare the evidence, not the stereotype.
Test whether reach, conversion, consistency, content, or networking better explains a growth gap between two profiles.
This lab uses only the numbers you enter. It cannot observe X's algorithm or establish that gender caused an outcome.
Put two growth stories on equal footing.
Use comparable windows. The result updates as you type and stays on this device.
Profile A
Observed inputsProfile B
Observed inputsNormalized result
Profile A follows / 1k impressions6.0
Profile B follows / 1k impressions4.0
Profile A converts 2.0 more follows per 1,000 impressions. Stronger consistency and networking are observable differences worth testing.
Hypothesis strength
Consistency
Networking
Content
Exposure
Strength means the profiles differ on that input. It is not causal probability.
What remains unknown
These observations cannot isolate ranking treatment, audience composition, post quality, or gender as a cause.
Your next fair test
For seven days, match posting cadence and content format. Vary reply effort only, then compare follows per 1,000 impressions.
Method
Conversion = follows ÷ impressions × 1,000. Hypothesis bars show normalized input gaps, capped at 100.
A better disagreement has a denominator.
Raw follower gains can look decisive while hiding unequal reach. Normalize the outcome, name what you cannot observe, and change one behavior at a time.
Could it be the algorithm?
Possibly, but profile-level impressions do not reveal why ranking systems distributed a post. Treat unexplained exposure as a question, not proof of favorable or unfavorable treatment.
Could it be content style?
Resonance ratings can document a hypothesis, but they remain subjective unless both profiles test matched formats, topics, and posting windows.
Could it be consistency?
Posts per week are observable. A matched-cadence test can isolate whether the apparent gap persists when frequency is held constant.
Could it be networking?
Replies and collaborations are measurable proxies for relationship-building. Compare them over the same period, then vary one behavior at a time.
Rank observable differences
Consistency, replies, collaborations, and resonance are candidates for testing. A large input gap is evidence of difference, not evidence of cause.
Export a falsifiable test
Carry the exact comparison, limitations, and a seven-day matched-behavior test into the conversation.