Common Statistical Techniques in Quantitative Research
A deep dive into common statistical techniques like correlation, regression, and chi-square tests, with real-world examples and visual outputs.
Understanding which statistical test to use is a common challenge. This guide builds upon the concepts in our foundational Guide to Quantitative Research.
Statistical techniques are the engines of quantitative research, allowing you to move beyond simple averages to find meaningful patterns.
They provide a structured way to test relationships, make predictions, and draw conclusions with a known level of confidence.
This guide demystifies some of the most common techniques, showing you what they are and when to use them.
Key Statistical Techniques
Each technique is designed to answer a different kind of question. Choosing the right one is the key to valid analysis.
Use Case:
"Is there a relationship between ad spend and website traffic?"
Use Case:
"How much does ad spend, email marketing, and social media engagement predict sales?"
Use Case:
"Is there a significant difference in purchase value between users who received offer A vs. offer B?"
Use Case:
"Is there a relationship between customer region and product category preference?"
Use Case:
"Can we reduce our 20-item customer satisfaction survey into 3 core factors like 'Service', 'Price', and 'Product'?"
Visualizing the Techniques
Seeing these techniques in action makes them easier to understand.
Correlation: Ad Spend vs. Sales
This scatter plot shows a clear positive correlation: as ad spend increases, sales tend to increase as well.
Regression: Sales Trend
This line chart models the sales trend over time, allowing us to forecast future sales based on past performance.
Statistical Techniques FAQs
Common questions about applying statistical tests in market research.
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