P-values and Confidence Intervals
In a hypothesis test, P-value assists in determining the meaning of results. The p-value is an alternative way to reject points to give the least level of significance at which the null hypothesis would be rejected (“P Values”). If a p-value is small this will mean that evidence is strong and in favor of the alternative hypothesis (“P Values”). They are calculated by using tables, spreadsheets, and statistical software. Confidence intervals are the probability that a population parameter will come in between two set values for a specific amount of time (Kenton, 2020). Confidence intervals can take any number of prospects, with commonalities of being 95% or 99% confidence level (Kenton, 2020).
Three reasons why it may be preferable to report a Confidence Interval over a P-value and example
Three reasons the Confidence Interval would be preferable over a P-value would be when using the confidence interval as a test or significance, giving an estimated range of values that most likely include an unknown population parameter and determining the width of the Confidence level. For an example of the (CI), the width would be determining the mean age in a population, the danger of developing a certain outcome (Kenton, 2020). Another example is the study that was conducted by Brinton and colleagues who studied infertile patients. Patients of this study went through evaluation for infertility and results showed a significantly higher rate of ovarian cancer among those being evaluated of those with ovarian cancer (Attia, n.d.). The general population was of female data using a standardized incidence ratio, (CI) (Attia, n.d.). Based on the data and using (CI), this meant that infertile females have a higher ovarian cancer incidence than non-infertile females.
References
Attia, A. (n.d.). EVIDENCE-BASED MEDICINE CORNER . Retrieved April 30, 2020, from
Kenton, W. (2020, March 12). Confidence Interval Definition. Retrieved April 30, 2020, from
P Values. (n.d.). Retrieved April 30, 2020, from
Expert Solution Preview
Introduction:
In this answer, we will discuss the importance of P-values and confidence intervals in hypothesis testing and statistical analysis. We will also explore the reasons why it may be preferable to report a confidence interval over a P-value in certain situations.
Answer:
P-values and confidence intervals are crucial statistical tools used in hypothesis testing to determine the meaning of results. A P-value is the probability of getting results as extreme as those observed in a sample, assuming the null hypothesis is true. A small P-value implies that evidence is strong in favor of the alternative hypothesis. Confidence intervals, on the other hand, are a probability that a population parameter will be within a particular range of values, and they are used to determine the estimated range of values that most likely includes an unknown population parameter. The confidence level is typically set at 95% or 99%.
Three reasons why it may be preferable to report a confidence interval instead of a P-value include:
1. Using the confidence interval as a test of significance: A confidence interval allows us to test whether the null hypothesis falls within the specified range of values. This approach is more informative than relying solely on P-values, which only indicate whether or not we should reject the null hypothesis.
2. Providing an estimated range of values: Confidence intervals provide a range of plausible values of population parameters, which gives us a better sense of the precision of our measurements and our level of confidence in the results.
3. Determining the width of the confidence level: The width of a confidence interval can provide useful information about the variability of the data. A wide confidence interval may suggest that the population parameter is highly variable, while a narrow one may indicate that it is more precise.
For example, in a study that evaluated the relationship between infertility and ovarian cancer, Brinton et al. (2004) found a significantly higher rate of ovarian cancer among infertile women than non-infertile women. By using a confidence interval, the researchers were able to estimate the incidence rate of ovarian cancer among infertile women and determine that it was higher than that of non-infertile women.
In conclusion, both P-values and confidence intervals are essential statistical tools used to evaluate the results of hypothesis testing. While P-values can be helpful in determining statistical significance, confidence intervals can provide a more complete picture of the results and the level of confidence we can have in them.
