What mistakes do PMs make when analyzing data?

Common mistakes PMs make when analyzing data:

• Ignoring data context. Without understanding business goals and data collection conditions, it's easy to draw incorrect conclusions.
• Blind trust in metrics. Metrics do not always reflect the real situation; it's important to verify their accuracy and relevance.
• Lack of segmentation. Analyzing data without breaking it down by key segments (e.g., user types) leads to averaged and less informative conclusions.
• Confirmation bias. Looking only for data that confirms existing hypotheses.
• Incorrect use of statistics. For example, not considering sample size, not testing the significance of differences.
• Inability to visualize data. Poor visualization hampers understanding and communication of results.

Example: A PM might see an increase in DAU and conclude that the product is growing, not noticing that the growth is due to a temporary promotion, while user retention is actually falling.