The New Arena

What do you seek?

  • Bread

    Votes: 23 17.6%

  • Circus

    Votes: 33 25.2%

  • Aggro

    Votes: 75 57.3%
  • Total voters
    131
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artworks-z1t6a2ZFKNpHzYkj-LeCAHw-t500x500.jpg
 

Clarky cat said:
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To overcome the limitations of sample size and p-values in statistical analysis, you can consider the following strategies:

  1. Increase Sample Size:
    • One of the most effective ways to address sample size limitations is to collect a larger sample of data. A larger sample size generally provides more precise estimates and can increase the statistical power of your analysis.
    • This can help reduce the risk of Type II errors (false negatives) and make it easier to detect smaller, yet practically significant, effects.
  2. Effect Size Emphasis:
    • Instead of solely relying on p-values, focus on effect sizes. Effect sizes quantify the magnitude of the relationship or difference in your data, making it easier to assess the practical significance of your findings.
    • Even with a small sample size, a large effect size can still be meaningful.
  3. Bayesian Analysis:
    • Consider using Bayesian statistical methods, which incorporate prior knowledge and allow for more flexible modeling. Bayesian analyses can provide more robust results with smaller sample sizes.
    • Bayesian credible intervals can also be used to complement p-values, providing a range of plausible parameter values.
  4. Replication:
    • Replicate your study with independent samples or in different settings. Replication studies can help validate your findings and increase confidence in the results.
  5. Meta-Analysis:
    • If possible, combine your study with others through meta-analysis. This can increase the effective sample size and provide a more comprehensive view of the research question.
  6. Adjust Significance Threshold:
    • Consider adjusting the significance threshold (alpha level) if necessary. A more stringent threshold (e.g., 0.01) can reduce the likelihood of false positives (Type I errors) but may increase the risk of false negatives (Type II errors).
  7. Use Confidence Intervals:
    • In addition to p-values, report confidence intervals for your estimates. Confidence intervals provide a range of plausible values, giving a sense of the precision of your results.
  8. Robust Statistics:
    • Employ robust statistical methods that are less sensitive to outliers or violations of assumptions, which can be particularly helpful when dealing with small samples.
  9. Expert Consultation:
    • Seek advice from statisticians or experts in your field to ensure that your statistical methods are appropriate and that you are interpreting the results correctly.
Remember that statistical analysis is not solely about achieving significant p-values but about drawing meaningful and robust conclusions from your data. Combining multiple approaches and focusing on the practical significance of your findings can help mitigate the limitations associated with small sample sizes and p-values.
 
dCahill17 said:
To overcome the limitations of sample size and p-values in statistical analysis, you can consider the following strategies:

  1. Increase Sample Size:
    • One of the most effective ways to address sample size limitations is to collect a larger sample of data. A larger sample size generally provides more precise estimates and can increase the statistical power of your analysis.
    • This can help reduce the risk of Type II errors (false negatives) and make it easier to detect smaller, yet practically significant, effects.
  2. Effect Size Emphasis:
    • Instead of solely relying on p-values, focus on effect sizes. Effect sizes quantify the magnitude of the relationship or difference in your data, making it easier to assess the practical significance of your findings.
    • Even with a small sample size, a large effect size can still be meaningful.
  3. Bayesian Analysis:
    • Consider using Bayesian statistical methods, which incorporate prior knowledge and allow for more flexible modeling. Bayesian analyses can provide more robust results with smaller sample sizes.
    • Bayesian credible intervals can also be used to complement p-values, providing a range of plausible parameter values.
  4. Replication:
    • Replicate your study with independent samples or in different settings. Replication studies can help validate your findings and increase confidence in the results.
  5. Meta-Analysis:
    • If possible, combine your study with others through meta-analysis. This can increase the effective sample size and provide a more comprehensive view of the research question.
  6. Adjust Significance Threshold:
    • Consider adjusting the significance threshold (alpha level) if necessary. A more stringent threshold (e.g., 0.01) can reduce the likelihood of false positives (Type I errors) but may increase the risk of false negatives (Type II errors).
  7. Use Confidence Intervals:
    • In addition to p-values, report confidence intervals for your estimates. Confidence intervals provide a range of plausible values, giving a sense of the precision of your results.
  8. Robust Statistics:
    • Employ robust statistical methods that are less sensitive to outliers or violations of assumptions, which can be particularly helpful when dealing with small samples.
  9. Expert Consultation:
    • Seek advice from statisticians or experts in your field to ensure that your statistical methods are appropriate and that you are interpreting the results correctly.
Remember that statistical analysis is not solely about achieving significant p-values but about drawing meaningful and robust conclusions from your data. Combining multiple approaches and focusing on the practical significance of your findings can help mitigate the limitations associated with small sample sizes and p-values.
Click to expand...
Oh look you can copy and paste. Bravo young critical thinker.

Can't explain it in your own words eh?
 
Inappropriate Behaviour
ashwuk76 said:
Oh look you can copy and paste. Bravo young critical thinker.

Can't explain it in your own words eh?
Click to expand...
Considering I have a degree heavily features statistics then probably yes 🤣 That copy and paste is the short answer as well.

Why would I invest that time without payment? Anyway out for a pint and food with a woman 😱😱😱 smell you losers later.
 

dCahill17 said:
Considering I have a degree heavily features statistics then probably yes 🤣 That copy and paste is the short answer as well.

Why would I invest that time without payment? Anyway out for a pint and food with a woman 😱😱😱 smell you losers later.
Click to expand...
The mere fact you didn't do that despite repeated asking... sure sure.

Hope you made sure you fixed the puncture first...

Or is this another delusion
 
dCahill17 said:
Considering I have a degree heavily features statistics then probably yes 🤣 That copy and paste is the short answer as well.

Why would I invest that time without payment? Anyway out for a pint and food with a woman 😱😱😱 smell you losers later.
Click to expand...
Hope she doesn’t get a puncture, that would be embarrassing for everyone.
BSc is a bachelor of science, you cannot claim this with your Bronze Swimming Certificate.
 

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