Testing for Normality
Ensuring Reliable Analysis
Reliable Analysis
Normality assessment is a pivotal step in statistical analysis, determining whether your data aligns with the assumptions of parametric tests. By employing both graphical methods, such as histograms, detrended plots, Q-Q plots, and P-P plots, and statistical tests like the Kolmogorov-Smirnov and Shapiro-Wilk tests, we ensure your data is ready for accurate and meaningful analysis.
Visual Insight and Statistical Precision
Graphical methods provide an intuitive snapshot of data distribution, highlighting deviations from normality at a glance. Statistical tests complement this by delivering precise metrics to confirm or reject normality assumptions.
Robust Results
Accurate assessment of normality isn’t just a technical requirement—it’s a critical step in building trust in your findings. Whether your data adheres to normality or requires alternative approaches, we guide you towards analysis strategies that uphold scientific integrity.
Let us safeguard the reliability of your research—because every great study begins with rigorous validation.