Quameth Cheat Sheet: Inferential Statistics & Probability

Cheat Sheet: Inferential Statistics Key Terms Inferential Statistics The process of using data from a sample to make inferences or predictions about a population. Estimation Making predictions about population parameters. Estimation involves calculating sample statistics...

Cheat Sheet: Inferential Statistics Key Terms Inferential Statistics The process of using data from a sample to make inferences or predictions about a population. Estimation Making predictions about population parameters. Estimation involves calculating sample statistics (like the sample mean) to estimate population parameters (like the population mean). Hypothesis Testing Assessing claims about a population. Hypothesis testing is used to determine if there is enough statistical evidence to support a hypothesis about a population. Correlation and Regression Analyzing relationships between variables. Measures the strength and direction of the linear relationship between two variables. Chi-Square and F Distribution Testing relationships in categorical data and comparing variances. Used for testing relationships between categorical variables. Nonparametric Statistics Statistical methods that do not assume a specific data distribution. Nonparametric methods do not assume a specific distribution for the data and are used when the assumptions for parametric tests are not met. This cheat sheet includes all key descriptions word for word from your text, providing a complete reference. Cheat Sheet: Key Probability and Distribution Terms DISTRIBUTIONS Deals with events that have specific outcomes that can be counted. PROBABILITY A variable that represents possible outcomes of a random event. RANDOM VARIABLES A variable that represents possible outcomes of a random event. Example: If you roll a die, the result (1, 2, 3, 4, 5, or 6) is a random variable. Each roll could be a different outcome. DISCRETE PROBABILITY DISTRIBUTION A probability distribution where the random variable can take on specific, countable outcomes. Example: Flipping a coin has a discrete distribution. The possible outcomes are only heads or tails. CONTINUOUS PROBABILITY DISTRIBUTION A probability distribution where the random variable can take on any value within a range. Example: Measuring weights or temperatures, where values are not limited to specific points that can be easily identified. BINOMIAL PROBABILITY DISTRIBUTION This distribution represents the probability of having a fixed number of successes in a specific number of trials, where each trial has two possible outcomes (e.g., success/failure). POISSON PROBABILITY DISTRIBUTION The Poisson distribution is a statistical model that calculates the probability of a specific number of events happening within a fixed timeframe or space, given a known average rate of occurrence. It assumes that these events occur independently of each other. HYPERGEOMETRIC PROBABILITY DISTRIBUTION A useful statistical tool for modeling situations where you want to determine the likelihood of a certain number of successes from a finite population when the draws are made without replacement. TRINOMIAL PROBABILITY DISTRIBUTION In a trinomial distribution, each trial results in one of three outcomes: often labeled as success, failure, and a third category (which could represent anything like "neutral," "other," or a specific type of success). NORMAL PROBABILITY DISTRIBUTION Also known as the Gaussian distribution or simply the normal distribution, is a continuous probability distribution that is symmetrical and describes how the values of a random variable are distributed. This cheat sheet provides clear and concise definitions for the important terms included in your text.