P-Values Got You Puzzled? Fixes for Consumer Psychology Research
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What is it?
This paper addresses the ongoing debate about data analysis & reporting practices in consumer psychology, particularly in light of the "replication crisis" and criticisms of null hypothesis significance testing (NHST).
The authors acknowledge the confusion surrounding various proposed solutions and offer five straightforward principles for researchers to guide their work in this new era of data analysis.
Five Principles:
Interpret p-values as continuous evidence: Don't rely on a strict p < 0.05 cutoff for "statistical significance." Instead, treat p-values as continuous measures of evidence against the null hypothesis, considering the specific context and multiple factors when interpreting them.
Be aware of model assumptions: Recognize that statistical procedures rely on assumptions about the data, and violating those assumptions can affect the validity of p-values. Consider alternative statistical methods (like Bayesian approaches) that may be more suitable for specific data and research questions.
Focus on theoretical generalizability, not statistical inference: Consumer psychology studies often use convenience samples, making formal population inferences inappropriate. Instead, prioritize building strong theories to guide the application of findings to new settings and populations.
Use multiple measures of evidence: Rely on diverse statistical measures (effect sizes, confidence intervals, Bayes factors, etc.) and collect data from multiple studies to enhance the strength of evidence. However, avoid assigning special privilege to any specific measure or method.
Report procedures and findings transparently: Disclose all relevant details of the research process, including data collection, processing, analyses, and exclusions, to allow for scrutiny and enable other researchers to assess the validity and robustness of the findings. Embrace Open Science practices for greater transparency.
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What do I need to know:
This paper provides a valuable framework for navigating the changing landscape of data analysis and reporting in consumer psychology.
Move beyond the rigid p-value cutoff: Interpreting p-values continuously and avoiding the term "statistically significant" can help reduce misinterpretations and the pressure for "significant" results.
Acknowledge model limitations and embrace flexibility: Statistical procedures are not perfect, and researchers should be flexible in choosing methods that best fit their data and research questions.
Theory is crucial for generalizability: Strong theories provide the foundation for applying findings to new contexts and guiding future research.
Transparency is paramount: Open Science practices and detailed reporting promote scrutiny and enhance the credibility of research findings.
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