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What do you do if you're faced with conflicting data in your Statistics career?

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Encountering conflicting data is a common hurdle in a statistics career. It's crucial to approach such discrepancies with a strategic mindset, ensuring that your decision-making process is informed and robust. When you're faced with data that doesn't align, it's not just a challenge; it's an opportunity to delve deeper into your analysis and uncover the true story behind the numbers. Remember, statistics is not just about data; it's about the interpretation and the narrative that emerges from that data. So, when you come across conflicting information, take a breath, equip yourself with your statistical tools, and prepare to embark on a detective-like investigation.

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Iain Brown PhD

Head of Data Science | Adjunct Professor | Author

1 Verify Data

When you're presented with conflicting data, your first step should be to verify the sources. Scrutinize the data collection methods, check for any possible errors in data entry or processing, and confirm that the datasets are complete and relevant. This due diligence can often resolve discrepancies as you may find that one set of data is outdated or collected under different conditions. It's like being a quality inspector; you need to ensure that every piece of information meets the highest standards before you can trust it to inform your decisions.

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Iain Brown PhD

Head of Data Science | Adjunct Professor | Author
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In my experience, verifying data often feels like detective work, where each clue can lead to a breakthrough or a dead-end. I recall a project where two datasets showed vastly different customer behaviour patterns. By diving deep into the data collection methods, we discovered that one set was gathered through an outdated platform, skewing the results significantly. This highlighted the importance of not just accepting data at face value but critically examining its origins and collection processes. It’s a meticulous task, but essential for building a reliable analysis foundation.

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Sachin Nomula

"Data Science Enthusiast | 2X Microsoft Azure certified | NLP, Deep Learning & Machine Learning | Aspiring Deep Learning Engineer | Committed to Harnessing Data for Innovative Solutions"
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Verifying data involves scrutinizing its sources for reliability, assessing collection methods, and ensuring consistency and quality. Check for anomalies, compare with similar datasets, and validate against prior knowledge. Documentation and peer review aid in confirming accuracy and transparency. Ethical considerations are crucial. Strive for reproducibility and comply with relevant regulations.

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2 Analyze Context

Understanding the context in which the data was collected is paramount. Investigate the environmental, temporal, and demographic factors that could have influenced the results. Different conditions can lead to varying outcomes, and recognizing these nuances is key to resolving conflicting data. This analysis helps you to interpret the data more accurately, much like a historian considering the circumstances of an event to understand its significance.

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Sachin Nomula

"Data Science Enthusiast | 2X Microsoft Azure certified | NLP, Deep Learning & Machine Learning | Aspiring Deep Learning Engineer | Committed to Harnessing Data for Innovative Solutions"
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Context analysis is pivotal in comprehending data meaningfully. It involves examining surrounding circumstances, historical trends, and environmental factors impacting data interpretation. Understanding the broader context enhances insight into patterns and relationships within the data. Consider socio-economic, cultural, and political contexts alongside technical aspects. Evaluate how external influences shape data collection, analysis, and presentation. Context analysis facilitates informed decision-making, aids in predicting future trends, and mitigates misinterpretation risks.

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3 Statistical Tests

Employing statistical tests is your next move to tackle conflicting data. These tests can help determine if the differences in data are statistically significant or if they could have occurred by chance. You might use t-tests, chi-square tests, or ANOVA (analysis of variance), depending on the type of data and the nature of the conflict. Think of these tests as your statistical detective kit, helping you to uncover the hidden truths in your data.

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Sachin Nomula

"Data Science Enthusiast | 2X Microsoft Azure certified | NLP, Deep Learning & Machine Learning | Aspiring Deep Learning Engineer | Committed to Harnessing Data for Innovative Solutions"
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Statistical tests infer population insights from sample data, gauging if observed differences are significant. T-tests compare means of two groups, ANOVA extends to multiple groups, while Chi-square tests categorical variable independence. Regression assesses relationships between predictors and outcomes. Correlation measures association strength. Non-parametric tests address data distribution issues. ANOVA with repeated measures analyzes changes over time. Logistic regression deals with binary outcomes. Survival analysis evaluates time-to-event data.

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4 Consult Experts

Sometimes, you need to call in the cavalry. Consulting with experts in the field can provide new insights or perspectives on your data conflict. These experts might identify factors you hadn't considered or suggest alternative analytical methods. Their experience can be invaluable, acting as a guide when you're navigating through the murky waters of conflicting datasets.

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5 Review Literature

A thorough review of existing literature can offer clues to resolving your data conflict. By examining previous studies and analyses, you may discover patterns or explanations that align with one set of data over another. This process is akin to building upon a foundation of knowledge, using the work of others to support or refute your findings and guide your interpretation.

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6 Reflect on Bias

Finally, reflect on any potential biases that could be affecting your interpretation of the data. Confirmation bias, for example, can lead you to favor information that supports your preconceptions. Being aware of these biases and actively working to mitigate them is essential for maintaining objectivity. It's about being your own critic, constantly questioning your methods and conclusions to ensure the integrity of your analysis.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What do you do if you're faced with conflicting data in your Statistics career?

1

2

3

4

5

6

7

1 Verify Data

2 Analyze Context

3 Statistical Tests

4 Consult Experts

5 Review Literature

6 Reflect on Bias

7 Here’s what else to consider

Statistics

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What do you do if you're faced with conflicting data in your Statistics career?

1

2

3

4

5

6

7

1 Verify Data

2 Analyze Context

3 Statistical Tests

4 Consult Experts

5 Review Literature

6 Reflect on Bias

7 Here’s what else to consider

Statistics

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