Stats Questions to Ask
Essential statistical questions to ask when analyzing data, conducting research, or evaluating studies to ensure accurate interpretation and meaningful insights.
1What is the sample size and how was it determined?
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What is the sample size and how was it determined?
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Why this works
Sample size affects statistical power and generalizability. Understanding how it was calculated ensures the study has adequate power to detect effects.
2What statistical tests were used and why were they chosen?
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What statistical tests were used and why were they chosen?
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Why this works
Different tests are appropriate for different data types and research questions. The choice should be justified based on data characteristics.
3What is the effect size and practical significance?
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What is the effect size and practical significance?
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Why this works
Statistical significance doesn't always mean practical importance. Effect size helps determine if results are meaningful in real-world terms.
4What are the confidence intervals and what do they tell us?
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What are the confidence intervals and what do they tell us?
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Why this works
Confidence intervals provide range estimates and show the precision of results, not just whether they're statistically significant.
5How was the data collected and what are the potential biases?
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How was the data collected and what are the potential biases?
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Why this works
Data collection methods can introduce systematic errors that affect the validity and reliability of statistical conclusions.
6What assumptions were made and how were they tested?
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What assumptions were made and how were they tested?
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Why this works
Statistical tests have underlying assumptions. Violations can lead to incorrect conclusions, so assumptions must be verified.
7What is the p-value and how should it be interpreted?
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What is the p-value and how should it be interpreted?
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Why this works
P-values are often misunderstood. They indicate the probability of observing results if the null hypothesis is true, not the probability the hypothesis is correct.
8Were multiple comparisons made and how was this handled?
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Were multiple comparisons made and how was this handled?
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Why this works
Multiple testing increases the chance of false positives. Proper correction methods must be applied to maintain statistical validity.
9What is the power of the study and what could affect it?
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What is the power of the study and what could affect it?
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Why this works
Statistical power determines the ability to detect true effects. Low power increases the risk of false negatives.
10How were outliers identified and handled?
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How were outliers identified and handled?
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Why this works
Outliers can significantly affect results. The approach to identifying and handling them should be transparent and justified.
11What is the correlation vs. causation distinction?
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What is the correlation vs. causation distinction?
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Why this works
Statistical association doesn't imply causation. Understanding this distinction is crucial for proper interpretation of relationships.
12What are the limitations of the statistical approach used?
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What are the limitations of the statistical approach used?
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Why this works
Every statistical method has limitations. Understanding these helps assess the reliability and generalizability of results.
13How was missing data handled and what impact might it have?
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How was missing data handled and what impact might it have?
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Why this works
Missing data can bias results. The approach to handling it affects the validity and generalizability of conclusions.
14What is the R-squared or coefficient of determination?
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What is the R-squared or coefficient of determination?
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Why this works
R-squared indicates how much variance is explained by the model, helping assess the strength of relationships and model fit.
15Were any transformations applied to the data and why?
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Were any transformations applied to the data and why?
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Why this works
Data transformations can improve model fit and meet statistical assumptions, but they affect interpretation of results.
16What is the difference between Type I and Type II errors?
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What is the difference between Type I and Type II errors?
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Why this works
Understanding these error types helps interpret statistical results and understand the trade-offs in hypothesis testing.
17How was the significance level chosen and is it appropriate?
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How was the significance level chosen and is it appropriate?
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Why this works
The alpha level affects the balance between Type I and Type II errors and should be chosen based on the consequences of each error type.
18What is the difference between parametric and non-parametric tests?
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What is the difference between parametric and non-parametric tests?
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Why this works
Choosing the right test type depends on data characteristics and assumptions. The wrong choice can lead to incorrect conclusions.
19How were the variables measured and what is their reliability?
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How were the variables measured and what is their reliability?
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Why this works
Measurement quality affects statistical results. Unreliable measures can lead to attenuated relationships and incorrect conclusions.
20What is the difference between descriptive and inferential statistics?
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What is the difference between descriptive and inferential statistics?
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Why this works
Understanding this distinction helps determine what conclusions can be drawn from data and what additional analysis might be needed.
Best Practices for Asking Statistical Questions
Expert tips and techniques for getting the most out of these questions.
Best Practices
Focus on Practical Significance
Don't just ask about statistical significance. Ask about effect sizes and practical meaning of results.
Question Assumptions
Always ask about the assumptions underlying statistical tests and how they were verified.
Consider the Big Picture
Ask about study design, data collection methods, and potential biases that could affect results.
Understand Limitations
Every statistical approach has limitations. Ask about them to better interpret results.
Question Sequences
The Study Design Sequence
The Statistical Analysis Sequence
The Interpretation Sequence
Common Pitfalls
Don't Confuse Statistical and Practical Significance
A result can be statistically significant but practically meaningless. Always ask about effect sizes.
Don't Ignore Assumptions
Statistical tests have assumptions. Violations can lead to incorrect conclusions, so always verify them.
Don't Assume Causation from Correlation
Statistical association doesn't imply causation. Always consider alternative explanations for relationships.
Don't Ignore Multiple Comparisons
Testing multiple hypotheses increases the chance of false positives. Always ask about correction methods.