1. Assess Source Reliability: Prioritize data from reputable, unbiased sources with strong credentials and consistent accuracy. 2. Examine Context and Bias: Identify any biases, methodologies, or contextual differences that could explain why the data conflicts. 3. Gather Additional Data: Seek out further information from independent, reliable sources to help clarify discrepancies. 4. Analyze Assumptions: Review each source’s assumptions or frameworks, as variations here often lead to conflicting data. 5. Select the Most Supported Conclusion: Choose the interpretation that aligns with the most credible evidence, documenting your reasoning and remaining open to updating as new data emerges.

When faced with conflicting data, evaluate the reliability and context of each source. Reassess the hypothesis, seeking patterns or insights that might reconcile the differences. If contradictions persist, consider refining or revising the hypothesis to better align with the evidence, ensuring a clear, evidence-based conclusion.

As a Business Systems Analyst at Spectrum, I helped transition my team from waterfall to agile, fostering collaboration and breaking down silos. Even after formally implementing agile, we struggled with results due to a lack of trust between developers and testers, limiting open communication. To address this, I did 2 things: 1. I introduced weekly bonding sessions with engaging activities to build rapport, making team members more comfortable reaching out to each other. 2. I encouraged my team to share their concerns and ideas in daily stand ups instead of waiting for retrospectives. This gave my team an opportunity to be more vocal. These efforts have transformed our team, and we’re now thriving as an agile unit.

1. Try to preprocess the data . 2. Give the priority to the data sources which are valid and having genuine data (Ex. Paid API's) 3. Fetching the patterns among the repeated data. 4. Reevaluate the hypothesis or try to change the predefined assumptions .

1- Evaluate Data Quality: Verify each data source and ensure collection aligns with study criteria, as small differences can lead to significant discrepancies. 2- Identify Biases: Check for biases in the data and your hypothesis to avoid unintended influence on analysis. 3- Explore Alternatives: Consider other interpretations of the results to uncover new insights or refine your theory. 4- Seek Additional Data: If possible, gather more data from independent sources for a stronger comparison. 5- Revise or Refine the Hypothesis: Adjust your hypothesis if needed to align with reliable data, leading to stronger conclusions.