Unlocking the Power of Statistical Hypothesis Testing: A Fresh ApproachNovember 20, 2024 •4:00 PM UTC
In this webinar, Lean Six Sigma Master Black Belt Kevin Keller will demystify the world of Statistical Hypothesis Testing. By testing for significance, you can unlock the ability to make precise and valid statistical inferences. However, in academic settings and most Lean Six Sigma classes, hypothesis testing is taught in a formal manner that can be daunting for newcomers. Kevin’s webinar will offer a refreshing alternative that simplifies these concepts while maintaining statistical rigor. In other words, Kevin will show you how testing for significance can lead to accurate statistical inferences without getting lost in complex jargon. Why is that important to you? Because your insights will be more accessible and impactful if you’re able to communicate your findings in a more business-friendly language. Don’t miss this opportunity to transform your understanding of Statistical Hypothesis Testing!
In this session we will cover:
- An introduction to Statistical Hypothesis Testing
- The importance of precise and valid statistical inferences
- Simplifying complex statistical concepts
- Avoiding complex technical language in your statistical discussions
- Making your statistical insights more accessible and impactful
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Kevin Keller is a professional statistician and quality professional with over 30 years of experience teaching, coaching, and leading Lean Six Sigma project teams from the shop floor to the enterprise levels. He began his career as a process engineer at Texas Instruments and then served in a Master Black Belt role at MEMC Electronic Materials for 15 years. Kevin advanced to manage quality systems for AB‐InBev and eventually accepted a Master Black Belt position for AB‐InBev's North American Zone. Kevin earned a BS in Chemical Engineering from Missouri University of Science and Technology and a Masters in Applied Statistics from The Ohio State University. He also is a certified Lean Six Sigma MBB.