Don't Let Statistical analysis Sink Your Dissertation!

Statistical analysis is a powerful tool in your dissertation toolbox, but using it incorrectly can weaken your research. Here's how to avoid common mistakes and ensure your analysis strengthens your arguments.

Mismatched Methods and Questions:

• Don't: Force a specific statistical test just because you have the data.

• Do: Choose the statistical test that best addresses your research question and data type.

Cherry-Picking the Data:

• Don't: Exclude data points that don't fit your desired outcome.

• Do: Report all relevant data and explain any exclusions with justification.

Misinterpreting P-Values:

• Don't: Solely rely on p-values to determine significance. They only tell part of the story.

• Do: Consider effect sizes and confidence intervals alongside p-values for a more complete picture.

Overlooking Assumptions:

• Don't: Apply a test without checking if your data meets its underlying assumptions (e.g., normality).

• Do: Verify assumptions or consider alternative tests if assumptions are violated.

Jargon Overload:

• Don't: Drown your reader in technical jargon when explaining your analysis.

• Do: Explain statistical terms clearly and focus on the meaning behind the numbers.

Ignoring Limitations:

• Don't: Present your statistical results as absolute truths.

• Do: Acknowledge limitations of your data collection or analysis methods.

Missing the Bigger Picture:

• Don't: Get lost in the numbers.

• Do: Connect your statistical findings back to your research question and implications.

Bonus Tip: Seek guidance! Don't hesitate to consult with a statistician or advisor if you're unsure about a specific analysis or interpretation.

By avoiding these common pitfalls, you can ensure your statistical analyses become a powerful asset in your dissertation, strengthening your research and effectively communicating your findings.