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A p-value below 0.05 marks rare results under the null hypothesis, hinting at significant findings.
The threshold of 0.05, set by Sir Ronald A. Fisher, balances Type I and II errors effectively.
A significant p-value doesn't speak to the effect's size. Always consider the effect size and context.
The 0.05 level is a convention, not a universal truth. Context may dictate a different threshold.
Confidence intervals complement p-values, offering a range within which the true effect likely falls.
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P-values don't confirm the alternative hypothesis; they merely suggest the null might be improbable.
Statistical results gain meaning within their specific research context, alongside existing scientific knowledge.
Look beyond p-values to the effect size, giving a fuller picture of the study's impact.
Narrow confidence intervals indicate a more precise estimate of the effect, adding to result reliability.
Recognize potential biases and apply adjustments to ensure the robustness of your findings.
Significant findings should be reproducible or replicable for added confidence and validation.
Dive into the full article to explore the nuances of statistical significance and p-values.