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Answer :
In statistics, the 95% confidence interval and P-value are tools used in medical research and other scientific fields to quantify uncertainty and make decisions based on study results.
95% Confidence Interval:
Definition: A confidence interval gives a range of values that is likely to contain the true parameter of a population. A 95% confidence interval means that if the study were repeated many times, 95% of the intervals calculated from those samples would contain the true parameter.
Purpose: It quantifies the precision of the estimated parameter (such as a mean or proportion). The width of the confidence interval reflects the uncertainty around the estimate. A narrower confidence interval indicates more precision.
Error/Bias: The confidence interval itself does not directly measure a specific type of error or bias but instead illustrates the potential range of the true parameter due to random sampling error. If a study is biased, it will affect the midpoint and potentially the width of the confidence interval.
P-value:
Definition: A P-value is the probability of observing data as extreme as, or more extreme than, what was observed in the study, assuming that the null hypothesis is true. It quantifies the evidence against the null hypothesis.
Purpose: It helps researchers decide whether the observed effect in the data is statistically significant. A commonly used threshold for significance is a P-value of less than 0.05.
Error/Bias: The P-value does not measure bias directly, but it helps determine the likelihood of committing a Type I error, which is rejecting a true null hypothesis (false positive). If a study has biases, they can influence the resulting P-values, such as through confounding factors or selection bias.
In summary, while the 95% confidence interval does not directly quantify bias, it helps understand the precision of estimates, and the P-value aids in understanding the significance of results which can be affected by biases in study design and data collection. Together, they are essential tools in assessing the reliability of study findings.
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