Types of Inferential Statistics
- Hypothesis Testing: Hypothesis testing involves making decisions about a population parameter based on sample data. It typically involves formulating a null hypothesis (H0) and an alternative hypothesis (Ha), collecting sample data, and using statistical tests to determine whether there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.
- Regression Analysis: Regression analysis is used to examine the relationship between one or more independent variables and a dependent variable. It helps in predicting the value of the dependent variable based on the values of the independent variables.
- Confidence Intervals: Confidence intervals provide a range of values within which the true population parameter is likely to fall with a certain level of confidence. For example, a 95% confidence interval for the population mean indicates that we are 95% confident that the true population mean falls within the interval.
What is Inferential Statistics?
In the world of data analysis, statistics plays a big role in helping us understand patterns and insights from raw data. Descriptive statistics help us summarize and describe data, while inferential statistics take us a step further by letting us make predictions and decisions about a larger group based on a smaller sample.
In this article, we’ll dive into inferential statistics, looking at why it’s important, how it works, and where it’s used.