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In other experiments or lab tests, quantitative data is preferable. If biochemists are working on determining the isoelectric point of an enzyme (the pH at which it has no net charge), they want a quantitative, numerical answer.
This article provides a visual, interpretable guide supported by real-world examples to help you choose the right statistical test depending on the nature and assumptions of your data, and the type of test or analytical task to perform.
A quantitative test is all about objectivity and group behaviour. It doesn’t concern the individual thoughts of participants, forsaking the context around the user action. These tests are helpful in identifying areas where things go wrong, but don’t help us understand why that is.
Statistical tests are used in hypothesis testing. They can be used to: determine whether a predictor variable has a statistically significant relationship with an outcome variable. estimate the difference between two or more groups. Statistical tests assume a null hypothesis of no relationship or no difference between groups. Then they ...
Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
Analysis of variance (ANOVA): tests for differences between the means of 2 or more groups; Pearson correlation co-efficient (Pearson’s R): tests for an association between two variables with an indication of strength; Regression or multiple regression: tests if an independent variable can predict another variable(s) Non-parametric tests
Quantitative tests are structured methodologies used to gather quantifiable data and derive meaningful insights. They rely on statistical and mathematical approaches to analyze numerical information systematically.
Quantitative data offer an indirect assessment of the usability of a design. They can be based on users’ performance on a given task (e.g., task-completion times, success rates, number of errors) or can reflect participants’ perception of usability (e.g., satisfaction ratings).
Statistical tests can also be classified based on their application in quantitative or qualitative research. This classification hinges primarily on the nature of the data being analyzed: quantitative research deals with numerical data, while qualitative research often involves non-numerical data.
Studies may be conducted to test a hypothesis and derive inferences from the sample results to the population. This is known as inferential statistics.