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Statistical Methods

Statistical methods are mathematical formulas, models, and techniques that are used in statistical analysis of raw research data. The application of these methods extracts information from research data and provides a detailed description of it.

There are two main types of statistical methods:

  1. Descriptive Statistics: This statistical method is used to summarize and interpret the pattern in data. It includes mean, median, mode, frequency distribution, measures of dispersion etc. Descriptive statistics methods help in the representation of data in a meaningful way.
  2. Inferential Statistics: This method allows you to make predictions or inferences about a population based on a sample of data taken from the population in question. Inferential statistics includes various testing methods like hypothesis testing, confidence intervals, and significance tests - t-test, chi-square test, ANOVA (Analysis of Variance) etc.

These methods are widely used in diverse fields including education, sociology, medicine, psychology, business, and health sciences. They help researchers make sense of their data, identify patterns, draw conclusions, and make predictions.

Other Related Statistical Methods topics include:

Here are other key topics in statistical methods:

  1. Regression Analysis: Regression Analysis is a statistical process for estimating the relationships among variables. It includes linear regression, multiple regression, and logistic regression.
  2. Probability Theory: Probability Theory topic deals with uncertainty. It includes the study of random events, probability distributions (like binomial, normal, and Poisson), and laws of probability.
  3. Time Series Analysis: Time Series Analysis involves analyzing data that is collected over time to identify patterns. This can be used for forecasting future events.
  4. Experimental Design: Experimental Design deals with planning, conducting, analyzing, and interpreting controlled tests to evaluate the factors that control the value of a parameter or group of parameters.
  5. Survival Analysis: Survival Analysis involves analyzing and modeling time-to-event data, often referred to as 'survival' data due to its origins in medical research.
  6. Bayesian Statistics: Bayesian Statistics is an approach to statistics that involves updating probabilities based on new data. Bayesian inference, a key part of this, contrasts with traditional 'frequentist' inference.
  7. Multivariate Analysis: Multivariate Analysis involves observation and analysis of more than one statistical outcome variable at a time.
  8. Non-parametric Statistics: Non-parametric Statistics are methods used when data doesn't fit a normal distribution, or when no specific distribution is assumed.

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