Confidently Conduct and Interpret Tests for Normality
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The document outlines the importance of testing for normality in data analysis, highlighting how many parametric statistical methods assume data follows a normal distribution and how violations of this can impact results. It details various inferential, descriptive, and visual methods for testing normality, as well as approaches to addressing non-normal data, such as removing outliers or using non-parametric tests. Additionally, the document promotes Statistics Solutions, a dissertation consulting service for graduate students, offering support and consultations.
Confidently Conduct and Interpret Tests for Normality
- 2. Additional Support Statistics Solutions is a full-service dissertation consulting company providing graduate students timely, editorial support for their dissertations and scholarly projects For information about our services, receive a complementary 30-min consultation available Mon-Fri 9-5 ET: https://app.hubspot.com/meetings/jeanine/dissertation-consultation info@StatisticsSolutions.com Phone: 877-437-8622
- 3. Overview Why is normality important? Methods for testing normality • Inferential • Descriptive • Visual How to address non-normal data Questions Need help with your dissertation? Call 727-442-4290
- 4. Why is normality important? Normal distribution: most frequent values are near the center (mean) of the distribution, and values farther from the center are less common Many parametric statistical analyses (e.g., t-test, ANOVA, linear regression) assume the data follow normal distributions Severe deviations from normality may affect the accuracy of the results Need help with your dissertation? Call 727-442-4290
- 5. Methods for testing normality Inferential Methods • Shapiro-Wilk or Kolmogorov-Smirnov tests • Tests determine if the data distribution is statistically significantly different from a normal distribution • A significant result (e.g., p < .05) indicates the data are different from a normal distribution • Pros • Objective and easy to interpret • Cons • No information about the shape of the distribution • Tests are too sensitive with high sample sizes Need help with your dissertation? Call 727-442-4290
- 6. Methods for testing normality Descriptive Methods • Skewness • Measure of distribution symmetry • Can be positive (skewed right) or negative (skewed left) • Values greater in magnitude than ±2.00 indicate high skewness • Kurtosis • Measure of distribution height • Higher values indicate a distribution with a higher center peak; lower values indicate a flatter distribution • Values greater than 3.00 indicate markedly high kurtosis • Mean vs. Median • A distribution with a mean that is markedly different from the median may not be normal • Can indicate if outliers are pulling the mean in the positive or negative direction Need help with your dissertation? Call 727-442-4290
- 7. Methods for testing normality Descriptive Methods • Pros • Provide an objective numerical value • Provide some information about distribution shape • Can be used with large sample sizes • Cons • Appropriate cutoffs for skewness and kurtosis are debatable • Still do not reveal the exact shape of the distribution Need help with your dissertation? Call 727-442-4290
- 8. Methods for testing normality Visual Methods • Histograms: show the frequencies of values in a distribution • A symmetrical “bell” shape indicates a normal distribution • Q-Q plots: plots the values of the distribution vs. expected normal values • A straight diagonal line indicates a normal distribution • Pros • Provide a clear picture of distribution shape • Provide more detail about nature and severity of skewness • Cons • Interpretation is subjective Need help with your dissertation? Call 727-442-4290
- 9. Methods for testing normality Need help with your dissertation? Call 727-442-4290
- 10. Methods for testing normality Need help with your dissertation? Call 727-442-4290
- 11. Methods for testing normality Need help with your dissertation? Call 727-442-4290
- 12. Methods for testing normality Need help with your dissertation? Call 727-442-4290
- 13. How to address non- normal data Remove outliers Transform the data • Logarithmic • Power or root transform (e.g., square root) • Reciprocal Run a non-parametric test Need help with your dissertation? Call 727-442-4290
- 14. Additional Support Statistics Solutions is a full-service dissertation consulting company providing graduate students timely, editorial support for their dissertations and scholarly projects For information about our services, receive a complementary 30-min consultation available Mon-Fri 9-5 ET: https://app.hubspot.com/meetings/jeanine/dissertation-consultation info@StatisticsSolutions.com Phone: 877-437-8622