Introduction Testing for normality is a critical step in data analysis, especially when using statistical methods that assume a normal distribution. This blog provides a comprehensive overview of several statistical tests used to assess the normality of your data, helping you ensure the validity of your analyses. What is a Normal Distribution? A normal distribution, or Gaussian distribution, is a bell-shaped curve that is symmetrical around the mean. Key characteristics include: Symmetry: The left and right sides of the curve are mirror images. Central peak: Most of the data points are concentrated around the mean. Tails: The tails approach, but never touch, the horizontal axis. Empirical Rule: Approximately 68% of data falls within one standard deviation of the mean, 95% within two, and 99.7% within three. Why Test for Normality? Many statistical methods, such as t-tests, ANOVA, and linear regression, assume that the data follow a normal distribution. Using these methods on non-no
The Rise of Artificial Intelligence: Applications and Implications Introduction: Artificial intelligence (AI) is no longer a futuristic concept; it's a present reality reshaping industries and societies worldwide. In this blog post, we'll explore the myriad applications of AI across various sectors and consider the profound implications of its widespread adoption. Understanding Artificial Intelligence: Define artificial intelligence and its key components, including machine learning, neural networks, and natural language processing. Discuss the evolution of AI from its inception to its current state, highlighting key milestones and breakthroughs. Applications of AI: a. Healthcare: Explore how AI is revolutionizing healthcare through early disease detection, personalized treatment plans, and predictive analytics. Highlight examples such as medical imaging analysis, drug discovery, and virtual health assistants. b. Finance: Discuss the role of AI in financial services, including