Why Should You Attend:
Being able to assess whether data is “normally distributed”, and to be able to "transform to normality" is critical to ensuring that a company's “valid statistical techniques” are “suitable for their intended use” (as required by the FDA). Therefore, it is critical to a company's success. Most users of statistics make the error of assuming normality, in order to simplify their statistical analyses. However, most data sets in industry are not normally distributed, and not noticing that oftentimes results in rejecting lots that should have passed, failing processes that actually met their validation criteria, or keeping products in R&D long after they should have been transferred to Manufacturing.
Such calculations include those for Student's t-Tests, ANOVA tables, F-tests, Normal Tolerance limits, and Process Capability Indices. Unless the raw data used in such calculations is “normally distributed”, the resulting conclusions may be incorrect.
Dimensional data (length, width, height) are typically normally distributed. But many other types of data sets are almost always non-normal, such as: tensile strength, burst pressure, and time or cycles to failure. Some non-normal data can be transformed into normality, in order to then allow statistical calculations to be valid when run on the transformed data.
Areas Covered in the Webinar:
Regulatory requirements
Binomial distribution
Historical origin of the Normal distribution
Normal distribution formula, histogram, and curve
Validity of Normality transformations
Necessity for transformation to Normality
How to use Normality transformations
Normal Probability Plot
How to evaluate Normality of raw data and transformed data
Significance tests for Normality
Evaluating the results of a Normality test
Recommendations for implementation
Recommended reference textbooks
Being able to assess whether data is “normally distributed”, and to be able to "transform to normality" is critical to ensuring that a company's “valid statistical techniques” are “suitable for their intended use” (as required by the FDA). Therefore, it is critical to a company's success. Most users of statistics make the error of assuming normality, in order to simplify their statistical analyses. However, most data sets in industry are not normally distributed, and not noticing that oftentimes results in rejecting lots that should have passed, failing processes that actually met their validation criteria, or keeping products in R&D long after they should have been transferred to Manufacturing.
Such calculations include those for Student's t-Tests, ANOVA tables, F-tests, Normal Tolerance limits, and Process Capability Indices. Unless the raw data used in such calculations is “normally distributed”, the resulting conclusions may be incorrect.
Dimensional data (length, width, height) are typically normally distributed. But many other types of data sets are almost always non-normal, such as: tensile strength, burst pressure, and time or cycles to failure. Some non-normal data can be transformed into normality, in order to then allow statistical calculations to be valid when run on the transformed data.
Areas Covered in the Webinar:
Regulatory requirements
Binomial distribution
Historical origin of the Normal distribution
Normal distribution formula, histogram, and curve
Validity of Normality transformations
Necessity for transformation to Normality
How to use Normality transformations
Normal Probability Plot
How to evaluate Normality of raw data and transformed data
Significance tests for Normality
Evaluating the results of a Normality test
Recommendations for implementation
Recommended reference textbooks
