Let's break down the steps involved in a structured manner:

1. **Clarifying Questions:**

What specific change was made to the Google app?

What is the main goal of this change? (e.g., increasing user engagement, improving conversion rates, etc.)

What metrics are relevant to measuring the impact of this change?

Are there any potential risks or downsides associated with this change?

2. **Prerequisites:**

**Success Metrics:**Metrics that directly reflect the positive impact of the change (e.g., user engagement, conversion rate).**Counter Metrics:**Metrics that might be negatively affected by the change (e.g., bounce rate).**Ecosystem Metrics:**Metrics that provide context and may indirectly be influenced by the change (e.g., overall app usage).**Control and Treatment Variants:**Control group (no change) and treatment group (with the change).**Randomization Units:**The units on which the randomization is applied (e.g., users, sessions).**Null Hypothesis:**Stating that the change will have no effect (no statistically significant difference).**Alternate Hypothesis:**Stating that the change will have a positive effect (a statistically significant increase).

3. **Experiment Design:**

Define the parameters for your experiment:

**Significance Level (α):**The probability of making a Type I error (false positive).**Practical Significance Level:**The minimum change you consider practically meaningful.**Power:**The probability of correctly detecting a true effect (1 - β).**Sample Size:**The number of randomization units needed in each group.**Duration:**The time the experiment will run.

Example values:

Significance Level (α): 0.05

Practical Significance Level: 1% increase in success metric.

Power: 0.8 (80%)

Sample Size: 10,000 users in each group (control and treatment).

Duration: 2 weeks.

4. **Running the Experiment:**

**Ramp-Up Plan:**Gradually exposing a subset of users to the change before a full rollout, ensuring there are no unexpected negative effects.**Bonferroni Correction:**If multiple metrics are being tested, adjust the significance level to avoid inflating the overall Type I error rate.

5. **Result to Decision:**

**Basic Sanity Checks:**Ensure that the control and treatment groups are similar at the baseline.**Statistical Test:**Conduct a statistical test (e.g., t-test, chi-squared test) to determine if the observed difference between the groups is statistically significant.**Recommendation:**If the change has a statistically significant positive impact and meets the practical significance level, you might recommend launching it. Otherwise, the null hypothesis isn't rejected, and the change may not be launched.

6. **Post Launch Monitoring:**

**Novelty/Primacy Effect:**Users may initially engage more with a new feature, but this could fade over time.**Network Effect:**The impact might grow as more users adopt the new feature and influence others to do the same.

Remember, A/B testing is an iterative process. Continuously monitor the metrics even after the launch to ensure that the change's impact aligns with your expectations. If it's not delivering the expected results, further adjustments or optimizations may be necessary.