Statistics

If probability is about predicting future events, statistics is about analyzing past events from data. Statistics is what connects the theoretical world of probability to the real-world data we work with in machine learning. It's broadly divided into two areas: Descriptive Statistics and Inferential Statistics. Descriptive statistics involves summarizing and describing the main features of a dataset. This includes calculating measures of central tendency, such as the mean (average), median (middle value), and mode (most frequent value), which give you a sense of the 'typical' data point. It also includes measures of variability or dispersion, like variance and standard deviation, which tell you how spread out the data is. Visualizing data through histograms and box plots is also a key part of descriptive statistics. Inferential statistics, on the other hand, is about drawing conclusions and making predictions about a larger population based on a smaller sample of data. This is where concepts like hypothesis testing and confidence intervals come in. For example, you might use a statistical test to determine if the difference in performance between two ML models is statistically significant or just due to random chance. Understanding statistics is crucial for everything from exploring your dataset (Exploratory Data Analysis) to designing experiments and evaluating the significance of your model's results. It provides the rigorous framework needed to make data-driven decisions.