Computing Confidence Interval Standard Error
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DisclaimerPublic Health TextbookResearch Methods1a - Epidemiology1b - Statistical Methods1c - Health Care Evaluation and Health Needs Assessment1d confidence interval sampling error - Qualitative MethodsDisease Causation and Diagnostic2a - Epidemiological Paradigms2b - convert confidence interval standard deviation Epidemiology of Diseases of Public Health Significance2c - Diagnosis and Screening2d - Genetics2e - Health calculate confidence interval variance and Social Behaviour2f - Environment2g - Communicable Disease2h - Principles and Practice of Health Promotion2i - Disease Prevention, Models of Behaviour ChangeHealth Information3a - Populations3b http://onlinelibrary.wiley.com/doi/10.1002/9781444311723.oth2/pdf - Sickness and Health3c - ApplicationsMedical Sociology, Policy and Economics4a - Concepts of Health and Illness and Aetiology of Illness4b - Health Care4c - Equality, Equity and Policy4d - Health EconomicsOrganisation and Management5a - Understanding Individuals,Teams and their Development5b - Understanding Organisations, their Functions and Structure5c - Management and Change5d - http://www.healthknowledge.org.uk/e-learning/statistical-methods/practitioners/standard-error-confidence-intervals Understanding the Theory and Process of Strategy Development5e - Finance, Management Accounting and Relevant Theoretical ApproachesFurther ResourcesFrameworks For Answering QuestionsGeneral Advice for Part APast Papers (available on the FPH website)Text CoursesEpidemiologyEpidemiology for PractitionersEpidemiology for SpecialistsHealth InformationApplications of health information for practitionersApplications of health information for specialistsPopulation health information for practitionersPopulation health information for specialistsSickness and health for practitionersSickness and Health Information for specialistsStatistical MethodsStatistical methods for practitionersStatistical methods for specialistsVideo CoursesIntroductionFinding and Appraising the Evidence1. Overall Introduction to Critical Appraisal2. Finding the Evidence3. Randomised Control Trials4. Systematic Reviews5. Economic Evaluations6. Making Sense of ResultsLearning from StakeholdersIntroductionChapter 1 – Stakeholder engagementChapter 2 – Reasons for engaging stakeholdersChapter 3 – Identifying appropriate stakeholdersChapter 4 – Understanding engagement methodsChapter 5 – Using engagement methods, P1Chapter 5 – Using engagement methods, P2Chapter 6 – Analysing the dataChapter 7 - Monitoring engagementChapter 8 - Evaluating engagementChapter 9 - Understanding the lessons learnedObesity Care Pathway Support PackageIntroductionInitiationStakeholde
on October 8, 2011 | Leave a comment This post covers the 3 applications of standard error required for the MFPH Part A; mean, proportions and differences between proportions (and their corresponding confidence intervals)… a) What is https://beanaroundtheworld.wordpress.com/2011/10/08/statistical-methods-standard-error-and-confidence-intervals/ the etandard error (SE) of a mean? The SE measures the amount of variability in the sample mean. It indicated how closely the population mean is likely to be estimated by the sample mean. (NB: https://www.graphpad.com/guides/prism/6/statistics/stat_confidence_interval_of_a_stand.htm this is different from Standard Deviation (SD) which measures the amount of variability in the population. SE incorporates SD to assess the difference beetween sample and population measurements due to sampling variation) Calculation of confidence interval SE for mean = SD / sqrt(n) …so the sample mean and its SE provide a range of likely values for the true population mean. How can you calculate the Confidence Interval (CI) for a mean? Assuming a normal distribution, we can state that 95% of the sample mean would lie within 1.96 SEs above or below the population mean, since 1.96 is the 2-sides 5% point of the standard confidence interval standard normal distribution. Calculation of CI for mean = (mean + (1.96 x SE)) to (mean - (1.96 x SE)) b) What is the SE and of a proportion? SE for a proprotion(p) = sqrt [(p (1 - p)) / n] 95% CI = sample value +/- (1.96 x SE) c) What is the SE of a difference in proportions? SE for two proportions(p) = sqrt [(SE of p1) + (SE of p2)] 95% CI = sample value +/- (1.96 x SE) Share this:TwitterFacebookLike this:Like Loading... Related This entry was posted in Part A, Statistical Methods (1b). Bookmark the permalink. ← Epidemiology - Attributable Risk (including AR% PAR +PAR%) Statistical Methods - Chi-Square and 2×2tables → Leave a Reply Cancel reply Enter your comment here... Fill in your details below or click an icon to log in: Email (required) (Address never made public) Name (required) Website You are commenting using your WordPress.com account. (LogOut/Change) You are commenting using your Twitter account. (LogOut/Change) You are commenting using your Facebook account. (LogOut/Change) You are commenting using your Google+ account. (LogOut/Change) Cancel Connecting to %s Notify me of new comments via email. Categories Critical Appraisal Epidemiology (1a) Health Policy Health Protection Part A Public Health Twitter Journal Club (#PHTwitJC) Scree
OF STATISTICS > Confidence intervals > Confidence interval of a standard deviation / Dear GraphPad, Confidence interval of a standard deviation A confidence interval can be computed for almost any value computed from a sample of data, including the standard deviation. The SD of a sample is not the same as the SD of the population It is straightforward to calculate the standard deviation from a sample of values. But how accurate is that standard deviation? Just by chance you may have happened to obtain data that are closely bunched together, making the SD low. Or you may have randomly obtained values that are far more scattered than the overall population, making the SD high. The SD of your sample does not equal, and may be quite far from, the SD of the population. Confidence intervals are not just for means Confidence intervals are most often computed for a mean. But the idea of a confidence interval is very general, and you can express the precision of any computed value as a 95% confidence interval (CI). Another example is a confidence interval of a best-fit value from regression, for example a confidence interval of a slope. The 95% CI of the SD The sample SD is just a value you compute from a sample of data. It's not done often, but it is certainly possible to compute a CI for a SD. GraphPad Prism does not do this calculation, but a free GraphPad QuickCalc does. Interpreting the CI of the SD is straightforward. If you assume that your data were randomly and independently sampled from a Gaussian distribution, you can be 95% sure that the CI contains the true population SD. How wide is the CI of the SD? Of course the answer depends on sample size (n). With small samples, the interval is quite wide as shown in the table below. n 95% CI of SD 2 0.45*SD to 31.9*SD 3 0.52*SD to 6.29*SD 5 0.60*SD to 2.87*SD 10 0.69*SD to 1.83*SD 25 0.78*SD to 1.39*SD 50 0.84*SD to 1.25*SD 100 0.88*SD to 1.16*SD 500 0.94*SD to 1.07*SD 1000 0.96*SD to 1.05*SD Example Data: 23, 31, 25, 30, 27 Mean: 1.50 SD: 3.35 The sample standard deviation computed from the five values is 3.35. But the true standard deviation of the population from which the values were sampled might be quite different. From the n=5 row of the t