Clarifying Questions:
Before designing the experiment, I would like to ask a few clarifying questions:
What are the specific emotions you are considering adding? This will help define the scope of the experiment and the emotional categories you want to test.
How do you plan to measure these emotions? Are you considering introducing new reaction buttons or another method?
What is the main goal of adding these emotions? Understanding the motivation behind this change will help in defining success metrics.
Prerequisites:
Success Metrics: One or more key metrics that directly measure the success of the experiment. For example, an increase in user engagement or time spent on the platform.
Counter Metrics: Metrics that might be negatively impacted by the change. For example, a decrease in the number of comments or likes.
Ecosystem Metrics: Metrics that reflect the broader impact on the Facebook ecosystem, such as user retention or ad revenue.
Control and Treatment Variants: The control group represents the current state with only Comment and Like options. The treatment group(s) will include the new emotions.
Randomization Units: Users should be randomly assigned to either the control or treatment group.
Null Hypothesis: There is no statistically significant difference in user engagement between the control and treatment groups.
Alternate Hypothesis: Adding new emotional reactions leads to a statistically significant increase in user engagement compared to the control group.
Experiment Design:
Significance Level: 0.05.
Practical Significance Level: Define a minimum acceptable change in engagement rate (e.g., 1% increase).
Power: 0.8.
Sample Size: Calculate using an effect size formula, assuming a small effect size (e.g., Cohen's d = 0.2).
Duration: Run the experiment for at least two weeks to capture potential weekly variations.
Effect Size: Assume a small increase in engagement rate (e.g., 0.5%).
Example Values: Significance Level = 0.05, Practical Significance Level = 1%, Power = 0.8, Sample Size = 10,000 in each group, Duration = 2 weeks.
Running the Experiment:
Ramp Up Plan: Initially roll out the new emotional reactions to a small subset of users to detect any technical or user experience issues before a full-scale launch.
Bonferroni Correction: If you are comparing multiple emotional reactions against the control, consider adjusting the significance level using the Bonferroni correction to account for multiple hypothesis testing.
Result to Decision:
Sanity Checks: Ensure that the control and treatment groups are similar in terms of demographics and usage patterns before analyzing the results.
Statistical Test: Conduct a t-test or a suitable statistical test to compare engagement metrics between the control and treatment groups.
Recommendation: If the p-value is less than the significance level and the effect size is practically significant, you may recommend implementing the new emotional reactions. Otherwise, stick to the current Comment and Like options.
Post Launch Monitoring:
Novelty/Primacy Effect: Users might initially engage with the new emotions due to their novelty. Monitor engagement over time to see if this effect fades.
Network Effect: Evaluate if the presence of emotional reactions for some users influences their connections to engage with the new reactions as well.
By following this structured experiment design, you can systematically assess the impact of adding new emotional reactions and make informed decisions based on data-driven insights.