Hypothesis testing is a cornerstone of quantitative research. It involves making predictions (hypotheses) about the relationships between variables and then using statistical tests to determine if these predictions hold true. Statistical analysis, which includes techniques like t-tests, chi-square tests, and ANOVA, is used to analyze the numerical data collected during the research process.

Experimental design refers to the framework of a research study that ensures the results are valid and reliable. It involves planning how to manipulate variables and measure their effects. Sampling is the process of selecting a subset of individuals from a population to represent the entire group. Proper sampling techniques are crucial for ensuring that the results of a study are generalizable to a larger population.

Surveys and questionnaires are common tools used in quantitative research to collect data from respondents. These tools allow researchers to gather large amounts of data quickly and efficiently. The responses are then quantified and subjected to statistical analysis to draw conclusions.

Correlation analysis examines the relationship between two variables to see if they are associated, while regression analysis goes a step further to determine the nature of the relationship. For example, regression analysis can help researchers understand how changes in one variable affect another, which is crucial for making predictions.

Quantitative research is extensively used in market research to understand consumer behavior, preferences, and trends. It provides businesses with data-driven insights to make informed decisions. Mixed-methods research, which combines both quantitative and qualitative approaches, provides a more comprehensive understanding of research questions by leveraging the strengths of both methodologies.