Generate a detailed validation loop plan for a new product feature.  The plan should include the following sections:</p>
<p>**1. Hypothesis:** Clearly state the hypothesis being tested regarding the new feature&#8217;s impact on user behavior or business metrics (e.g., &#8220;Adding a personalized recommendation engine will increase user engagement by 15%&#8221;).</p>
<p>**2. Metrics:** Define specific, measurable, achievable, relevant, and time-bound (SMART) metrics to track the success or failure of the hypothesis.  Include both leading indicators (early signals of success) and lagging indicators (ultimate business outcomes).  Provide examples of how these metrics will be measured.</p>
<p>**3. Target Audience:** Identify the specific user segment the new feature is targeted towards.  Provide details about their demographics, behaviors, and needs.</p>
<p>**4. Experiment Design:** Outline the experiment design to test the hypothesis. This should include the following:<br />
    * **A/B Testing:** If applicable, describe the control group and the variation group.  Specify how users will be assigned to each group.<br />
    * **Sample Size:** Determine the appropriate sample size required for statistically significant results.<br />
    * **Duration:** Specify the duration of the experiment.<br />
    * **Data Collection Methods:** Describe how data will be collected (e.g., analytics dashboards, surveys, user interviews).</p>
<p>**5. Analysis Plan:** Detail how the collected data will be analyzed to determine whether the hypothesis was supported or refuted. Include specific statistical tests or methods to be used.</p>
<p>**6. Decision Criteria:** Define clear criteria for deciding whether to launch the new feature based on the experimental results.  Consider both statistical significance and practical significance.</p>
<p>**7. Contingency Plan:** Outline a plan for what will happen if the hypothesis is not supported.  This might include modifying the feature, abandoning the feature, or conducting further experiments.</p>
<p>**Example:** Let&#8217;s say the new feature is a &#8220;dark mode&#8221; option. The hypothesis could be: &#8220;Implementing a dark mode option will increase daily active users by 10% among users aged 25-45.&#8221; The metrics could include daily active users, session duration, and user feedback surveys. The experiment could involve A/B testing with a control group without dark mode and a variation group with dark mode.  The analysis plan could involve comparing the daily active users in both groups using a t-test.</p>
<p>The output should be a structured document suitable for sharing with a product development team.