Hypothesis Testing

Hypothesis testing is a statistical method used to determine if there is enough evidence to reject a null hypothesis (H0) in favor of an alternative hypothesis (𝐻𝑎 ). By analyzing sample data, the process involves setting a significance level, calculating a test statistic, and comparing it to a critical value or P-value to make data-driven decisions. Common tests include t-tests, z-tests, and chi-square tests, applied in diverse fields to validate assumptions and drive conclusions.

Disaggregated data from surveys

Disaggregated data from surveys involves breaking down survey responses into smaller, more specific groups based on different characteristics or categories. This allows for more detailed analysis and helps to identify patterns, trends, or disparities that may not be visible in the aggregated data. The process of disaggregation can reveal important insights, particularly when working with diverse populations or when the goal is to make data-driven decisions that are inclusive and representative of different groups.

Understanding Standard Error and Confidence Intervals

In statistics, standard error and confidence intervals are fundamental concepts that help us draw conclusions about population parameters based on sample data. These tools are essential for estimating population means, proportions, and other characteristics in a way that reflects the uncertainty inherent in working with samples. Let’s dive into what these concepts mean and how they are used.

Simple Linear Regression

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. Its main goal is to find the best-fitting linear equation that can predict the dependent variable based on the independent variables.