Descriptive statistics summarize data by finding the average (mean), middle value (median), and most common value (mode), while also measuring how spread out the data is using standard deviation. Probability helps predict the likelihood of events, aiding in decision-making when outcomes are uncertain. Inferential statistics allows conclusions about a larger group based on a smaller sample. Distributions show how data is spread, with the normal distribution being a common example. Correlation shows relationships between variables, while causation indicates one directly influences another. Sampling involves selecting a small group from a population to make broader conclusions. Regression analysis is used to predict outcomes and understand relationships between variables, helping build predictive models in data science.