Here's a framework for designing an A/B testing experiment for a product or feature, along with an example using Facebook's launch of Reactions as a new feature:

**1) Clarifying Questions:**

Before diving into the experiment design, it's crucial to ask some clarifying questions to ensure a clear understanding of the goals and constraints. These might include:

What is the specific objective of the A/B test?

Who is the target audience for this feature?

Are there any ethical considerations or potential risks?

What is the expected timeline for this experiment?

What resources are available for the experiment?

**2) Prerequisites:**

**Success Metrics**: Define the primary metrics that will measure the success of the new feature (e.g., increased user engagement).**Counter Metrics**: Identify any secondary metrics that may be affected by the change but are not the primary focus (e.g., time spent on the platform, page load time etc.).**Ecosystem Metrics**: Consider how the feature might impact the broader product ecosystem (e.g., effects on other features or user behavior).**Control and Treatment Variants**: Define the control group (existing feature) and treatment group (new feature).**Randomization Units**: Decide how users will be assigned to the control and treatment groups (e.g., random user assignment).**Null Hypothesis and Alternate Hypothesis**: Formulate a null hypothesis (e.g., "The new feature has no significant impact on user engagement") and an alternate hypothesis (e.g., "The new feature increases user engagement").

**3) Experiment Design:**

**Significance Level (α):**Typically set at 0.05, representing the probability of making a Type I error.**Practical Significance Level:**Define the minimum meaningful change in the success metric.**Power:**Choose the desired statistical power (often 0.80), representing the probability of detecting a true effect.**Sample Size:**Calculate the required sample size based on the significance level, practical significance level, power, and expected effect size. For example, if we expect a 5% increase in user engagement, we can calculate the sample size needed to detect this change.**Duration:**Determine the duration of the experiment based on the sample size and expected user behavior.**Effect Size:**Estimate the expected effect size of the feature (e.g., the percentage increase in user engagement).

**4) Running the Experiment:**

**Ramp Up Plan**: Gradually roll out the new feature to a small subset of users before full deployment.**Bonferroni Correction**: Apply correction if multiple tests are being conducted to control the familywise error rate.

**5) Result to Decision:**

**Basic Sanity Checks**: Ensure that the data is reliable, and there are no technical issues affecting the results.**Statistical Test**: Apply the appropriate statistical test (e.g., t-test or chi-squared test) to compare the control and treatment groups.**Recommendation**: Based on the results, make a recommendation to either launch the feature, tweak it, or abandon it.

**6) Post Launch Monitoring:**

**Novelty/Primacy Effect**: Be aware of initial spikes in user engagement that may be driven by novelty and account for this in long-term analysis.**Network Effect**: Monitor how the new feature influences other features or user behavior and adapt strategies accordingly.

**Example - Facebook launching Reactions as a new feature:**

**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.