You're facing skepticism about your statistical results. How do you convince your peers of their reliability?

To counter skepticism about my statistical results, I focus on clearly explaining the methodology. I describe the steps taken in data collection, analysis, and the assumptions involved. This approach fosters trust and demonstrates the results' reliability. From my experience, thorough and transparent explanations are key to convincing others.

Listening critics sincerely is crucial. If the results contradicts with common sense of experts, listen and think throughly. Understanding the context is as important as understanding the math behind methods.

Well, to start with, skepticism about my results is not a problem. In fact, my best friend in my career has always been the voice in my own head saying, "Are you sure this is right? Did you really check this other thing? Did you miss a step?" That annoying voice turns out to be right a lot of the time, and so I can hardly afford to be annoyed by other people's voices. It's an opportunity to ask myself: do they have a point? If they are informed people, what makes them skeptical? Even if I'm right, what is it about my results that doesn't match their own experience? Is it possible that my results are statistically correct but not useful to these critics because they are in different circumstances from what my data came from?

Address Potential Biases: Acknowledge any limitations or potential biases in your study. Discuss how you have mitigated these issues or why they do not significantly impact your results. Peer Review and Replication: Encourage peer review and replication of your study. If others can replicate your results using the same methods, it adds credibility to your findings. Engage in Open Dialogue: Be open to questions and critiques from your peers. Engaging in constructive discussions can help address any concerns and demonstrate your confidence in your results. By combining these strategies, you can effectively communicate the reliability of your statistical results and address any skepticism from your peers.

One way is to publish the data you based your inference on, together with a thorough explanation of how you generated said data. This needs to go along with the statistical model you use for your inference.

I always try to make sure I cite the source of the data for the statistical test. This can include SQL database and specific schemas/tables, CRM system, or a campaign manager. Second thing I believe is important is to not be scared to always state your assumptions - nothing is perfect with data, testing, and distributions - but if you make relevant assumptions it will be easier for business people to understand where you are coming from. For example: Based on the data and after looking at the histogram, I assume this to be (approximately) normally distributed.

The statement really captures the essence. Validating our data sources is absolutely crucial in any scientific study. We need to look at more than just how trustworthy the sources are. We also have to check the data itself using sound methods, think about the difficulties in doing this, and consider the ethical side of things. Only by taking all these factors into account can we make sure our research is top-notch and people believe our results. And let's be honest, we've all been there, struggling with these challenges at some point.

I had to convince my team of the reliability of the results, and I knew validating the data sources was key. I made sure to highlight that the datasets I used came from well-known, reputable providers, and I provided examples of how these datasets had been used in other respected studies. I also walked the team through the data cleaning and preparation steps I took to ensure the data was accurate and ready for analysis. By emphasizing the credibility of my sources and the meticulous preparation process, I was able to strengthen my case and gain their trust.

Data is at the core of our work. Take your time to understand every attribute. Scope of variables: - Reliability of source system (e.g. SAP vs. Excel)? - What is the reference time interval? - What unit is used? - Compare with different data sources. - Granularity of variable e.g. does this variable aggregate different types (for fuel volume: e.g. gasoline, diesel, etc.) Distribution: - Are there very small, very large, exact values values? - Are number of modes (peaks in distribution) expected? - Are there gaps in the distribution? - For categorical attributes check if the categories make sense. Does the distribution of categories make sense? Data Analytics is a team effort! Give credit to all individuals who supported you!

In one analysis, I knew that the reliability of my results hinged on the robustness of the statistical models I used. I took the time to clearly explain to my peers why I chose specific models and the assumptions behind them. For instance, I discussed why a linear regression model was appropriate and how I tested for its assumptions, like homoscedasticity and normality. Additionally, I performed sensitivity analyses to see how my results held up under different conditions. By demonstrating that my conclusions were consistent across various scenarios, I was able to reassure my peers of the robustness and credibility of my findings.

Eventually it is a matter of avoiding the dichotomy: analytical versus intuition. It could be done by integrating those two perspectives into, for instance, a weighted sum model, where the results from the statistics techniques and the perception from the expert could be mixed and eventually adjusted by means of the weights. In that way, neither the efforts from the analytical side nor the experience from the expert would be disregarded, which increases the chances of a successful implementation of the decision.

Achieving reliable results through sound statistical analysis requires a mix of experienced and fresh-minded statisticians. However, to convince your peers, you need a well-crafted analytics report with intuitive visuals that tell the complete story. By layering your analysis—where each layer provides deeper insights—you can help those less familiar with statistics gain a clearer understanding. A well-structured report is essential to this process.

To build trust in an analysis, repeat it with random subsamples (e.g., 50% of the data) or use different algorithms (e.g., linear regression, random forest, XGBoost). If your results change significantly, your methods might not be robust and could be influenced by the algorithm or sample. With programming languages like Python, you can use a baseline model to automatically check robustness. Another method is to test your model with extreme values to see how it predicts. Finally, examine cases with large prediction errors to understand what's going on.