Lecture 8 Confidence Interval PDF

DENT4407 Research Methods in Dentistry Lecture 8: Confidence Interval P (Probability)-Value Assoc. Prof. Magrur Kazak [email protected] 21.11.2023 Resources Confidence Interval Samples are collected to make estimations and decisions about the population. composite resin samples n: 12...

DENT4407 Research Methods in Dentistry Lecture 8: Confidence Interval P (Probability)-Value Assoc. Prof. Magrur Kazak [email protected] 21.11.2023 Resources Confidence Interval Samples are collected to make estimations and decisions about the population. composite resin samples n: 12 Confidence Interval There are two types of estimations in statistics. 1-point estimation 2-interval estimation 1-Estimating a parameter of the population using sample statistics is called point estimation. Estimates such as median, mode, and arithmetic mean are point estimates. Confidence Interval Most of the time, we cannot know the accuracy of our point estimation. Therefore, we need a measure of how confident our estimation is. 2-Interval estimate is the name given to the confidence interval consisting of the lower and upper limits of the estimated statistic with a certain probability. Confidence Interval A confidence interval shows the probability that a parameter will fall between a pair of values around the mean. The concept of Confidence Interval was developed to demonstrate reliability in an acceptable way. Confidence Interval 90% The confidence interval band is desired to be as high as possible. 99% Confidence Intervals are expressed as percentages (%). Confidence Intervals are never 100%. Commonly used confidence levels are 90%, 95% or 99%. 95% In an article, the Confidence Interval should be specified. 100% Confidence Interval Calculating A Confidence Interval Step 1: Determine the sample size (n). Step 2: Determine the samples' means (x). Step 3: Determine the sample standard deviation (s). Step 4: Determine the confidence level (Z) Confidence Interval Reporting Confidence Intervals We always present Confidence Intervals in the manner shown below: 95% CI [LL, UL] LL: Lower limit of the Confidence Interval UL: Upper limit of the Confidence Interval P (Probability)-Values P (Probability)-Values P value is used to determine the existence of statistical significance and, if any, to determine the level of evidence of the existing difference. As a result of each statistical test, a P value is calculated and İt is the outcome of the statistical test, which is a probability. A p-value of less than 0.05 is considered "statistically significant" in the medical literature. Strong evidence is correlated with low p-values. The results are deemed "statistically significant" if the p-value falls below a certain threshold. P (Probability)-Values Interpretation of P-Values P (Probability)-Values Evaluation of P-Values In studies in health sciences, a p-value of less than 0.05 is generally considered sufficient for significance. It would be more informative for the reader to give the exact p-value (e.g. p= 0.095) in the article instead of just giving the significance level of p (e.g. p>0.05). If the P value is less than the significance level we determined, a difference is found. If the P value is greater than the significance level we set, there is no difference. P (Probability)-Values In medicine, clinical and statistical significance differ from each other. Statistical significance is making decisions or making predictions about the population of the same patients/materials, based on the sample of patients/materials. After statistical significance is tested, clinical significance is discussed in order to decide the clinical usability and usefulness of a significant finding. For a finding to be clinically significant, it must first be statistically significant. However, not every statistically significant finding may be clinically significant. Example 1 Hypothesis/hypotheses The first step in conducting a scientific study is to create the hypothesis/hypotheses to be tested. number of samples (n) It is necessary to use the correct sampling technique to create groups similar to the population. Before any research statisticians should make a POWER ANALYSIS. A power analysis is a calculation used to estimate the smallest sample size needed for an experiment. Example 2 confidence interval Example 3 hypotheses Example 3 number of samples (n) Accepted or rejected hypotheses