Making Data Driven Decisions using Statistical Hypothesis Testing

This training program will cover statistical hypothesis testing concepts including: null and alternate hypotheses, test statistics, p-values, confidence intervals, confidence levels, power, power curves, and sample sizes. Several types of hypothesis tests will be covered such as 1 and 2-sample means tests, tests of variances, and tests of proportions. Equivalence tests will also be discussed. The importance of selecting appropriate sample sizes will be stressed.

Why Should You Attend:

Many engineers, scientists, and business analysts struggle with the application of statistical methods when analyzing data to making decisions. Non statisticians frequently seek help in tasks such as determining appropriate sample sizes, interpreting tests results, and distinguishing statistical differences from practical differences.

This webinar will lay the groundwork for a deeper understanding of statistical hypothesis tests. Key concepts and terminology underlying statistical hypothesis tests will be clearly explained. Then, the applications of various different hypothesis tests along with important assumptions are presented. The focus will be on the correct interpretation and presentation of results rather than mathematical details.

Areas Covered in the Webinar:

• Understand hypothesis testing definitions and methodology
• Select appropriate hypothesis tests for specific applications
• Understand key assumptions in specific tests
• Interpret results of hypothesis tests
• Determine appropriate sample sizes for conducting studies
• Learn how to supplement statistical results with graphical methods to illustrate conclusions
• Use correct language when presenting results

Who Will Benefit:

• Product development personnel
• Research and development personnel
• Quality personnel
• Product/process engineers
• Personnel utilizing data to make decisions and improve processes