Ten Common Misconceptions About Quantitative Research
Summary
This brief article presents ten questions about common assumptions in quantitative research. They concern whether researchers must be highly skilled in statistics, whether borrowed methods can be adopted without adaptation, and whether more variables, more complex tests, longer questionnaires, or larger samples automatically make a study stronger. It also asks whether nonsignificant findings or simple methods such as frequency counts and chi-square tests lack value, and whether a topic with little prior literature is necessarily worth pursuing.
The document frames quantitative research as including both observational and intervention-based approaches, and suggests that researchers can be discouraged by misconceptions about statistics. However, it gives the questions without answers, supporting evidence, or practical guidance for resolving them. It is therefore useful as a checklist of research-design concerns, not as a detailed methods lesson. For trading research, the prompts encourage scrutiny of model complexity, sample size, measurement choices, and how statistical significance relates to the value of a finding.
Key ideas
- Quantitative research can involve observational or intervention-based designs.
- The article challenges the assumption that quantitative researchers must master highly complex statistics.
- It raises whether adding variables or using more complex methods necessarily improves a study.
- It questions whether larger samples alone guarantee stronger inferences.
- The article lists research concerns but does not provide answers, evidence, or recommendations.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.