* Research Basics *

Problem: Using too small a sample size reduces the ability to detect meaningful effects or draw reliable conclusions. Conversely, an improperly selected large sample may waste resources without improving validity.

Why it matters: Insufficient sample sizes undermine statistical power, making it difficult to distinguish real patterns from random noise. Biased or unrepresentative samples limit generalizability.

How to avoid it:

• Conduct a power analysis to determine appropriate sample size.
• Ensure your sampling method aligns with the population you want to study.
• Consider stratification or oversampling if key subgroups are small or underrepresented.
• Seek guidance from faculty, statisticians, or methodological texts when in doubt.

Problem: Identifying a relationship between two variables does not necessarily mean one causes the other. This confusion is especially common in observational and survey-based research.

Why it matters: Misinterpreting associations can lead to false conclusions, poor decision-making, and misleading policy or practice recommendations.

How to avoid it:

• Use experimental or quasi-experimental designs when causal claims are needed.
• Clearly state limitations of your design in drawing causal inferences.
• Consider third variables, confounders, or reverse causality when analyzing relationships.
• Frame findings in terms of association unless you’ve met the criteria for establishing causality.

Problem: Personal expectations or assumptions can influence how data is collected, coded, interpreted, or reported—especially in qualitative studies or open-ended methods.

Why it matters: Bias distorts findings and compromises objectivity, reducing trustworthiness and transparency. It may also affect replication and credibility.

How to avoid it:

• Use standardized instruments and protocols where possible.
• Employ blind or double-blind procedures to minimize influence.
• Keep detailed field notes or audit trails in qualitative research.
• Reflect on your own positionality and disclose any potential conflicts of interest.

Problem: Jumping to a single interpretation or explanation without considering other plausible options can oversimplify complex phenomena.

Why it matters: Overlooking rival explanations leads to incomplete analysis and may exaggerate the strength of your argument.

How to avoid it:

• Use theoretical frameworks that encourage multiple perspectives.
• Test competing hypotheses when analyzing data.
• Discuss limitations and alternative interpretations in your conclusion section.
• Review your findings with peers or advisors who may offer fresh insights or critiques.

Problem: Drawing broad conclusions from narrow case studies or localized data can exaggerate the applicability of your findings.

Why it matters: Without careful framing, overgeneralization can mislead audiences and weaken the credibility of your work.

How to avoid it:

• Clearly define the scope and boundaries of your study.
• Use phrases like “in this context” or “among this group” to avoid sweeping claims.
• Where appropriate, use comparative or cross-case methods to increase relevance.
• Treat case studies as illustrative rather than definitive unless replicated elsewhere.

Problem: Failing to clearly document research steps, data sources, or analysis procedures reduces replicability and trust.

Why it matters: Transparent reporting is essential for peer review, reproducibility, and ethical accountability.

How to avoid it:

• Keep detailed records of your research process from planning to analysis.
• Report your methodology with enough detail for others to replicate.
• Use appendices or supplementary materials for extensive data or procedures.
• Consider preregistering your study design, especially in experimental research.

Problem: Ignoring ethical responsibilities—such as informed consent, confidentiality, or data security—can harm participants and invalidate your work.

Why it matters: Ethical breaches can damage reputations, invalidate data, and result in institutional or legal consequences.

How to avoid it:

• Submit your research for Institutional Review Board (IRB) approval when working with human subjects.
• Use secure platforms for storing sensitive information.
• Design consent forms that are clear, accessible, and appropriate for your audience.
• Think proactively about risks and how to minimize them.

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