Binomial proportion confidence interval on Wikipedia.

(Version 20.110 accessed June 27, 2022) See also

Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

Zhou XH, NA Obuchowski, DK McClish (2002) Statistical methods in diagnostic medicine.

Metz CE (1978) Basic principles of ROC analysis.

Mercaldo ND, Lau KF, Zhou XH (2007) Confidence intervals for predictive values with an emphasis to case-control studies.

Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Griner PF, Mayewski RJ, Mushlin AI, Greenland P (1981) Selection and interpretation of diagnostic tests and procedures.

Gardner IA, Greiner M (2006) Receiver-operating characteristic curves and likelihood ratios: improvements over traditional methods for the evaluation and application of veterinary clinical pathology tests.

Altman DG, Machin D, Bryant TN, Gardner MJ (Eds) (2000) Statistics with confidence, 2 nd ed.

2000.Ĭonfidence intervals for the predictive values are the standard logit confidence intervals given by Mercaldo et al. Sensitivity, specificity, disease prevalence, positive and negative predictive value as well as accuracy are expressed as percentages.Ĭonfidence intervals for sensitivity, specificity and accuracy are 'exact' Clopper-Pearson confidence intervals.Ĭonfidence intervals for the likelihood ratios are calculated using the 'Log method' as given on page 109 of Altman et al. = Sensitivity × Prevalence + Specificity × (1 − Prevalence)

Accuracy: overall probability that a patient is correctly classified.

Negative predictive value: probability that the disease is not present when the test is negative.

Positive predictive value: probability that the disease is present when the test is positive.= False negative rate / True negative rate = (1-Sensitivity) / Specificity Negative likelihood ratio: ratio between the probability of a negative test result given the presence of the disease and the probability of a negative test result given the absence of the disease, i.e.= True positive rate / False positive rate = Sensitivity / (1-Specificity) Positive likelihood ratio: ratio between the probability of a positive test result given the presence of the disease and the probability of a positive test result given the absence of the disease, i.e.Specificity: probability that a test result will be negative when the disease is not present (true negative rate).Sensitivity: probability that a test result will be positive when the disease is present (true positive rate).Enter the sample size, the observed mean and standard deviation for the sample 1.(*) These values are dependent on disease prevalence.You have more information in the following post. How you can determine if the difference is due to sample size effects or it is real? By doing a significant test. Sample Size and Power calculations include Population Survey. After surveying 1.000 mums from each country you get that mothers from country A have 2,4 children on average and mothers from country B a 3,1. StatCalc is a statistical calculator that produces summary epidemiologic information. For example, you want to know if mothers from the country A have more children on average than mothers in country B. Significant difference calculator between meansĬheck if the observed difference between two samples is significant regarding an average value. The calculator evaluates if the difference is significant and which is the minimum difference you have to observe to get this significance. so there it is! Result is re-calculated each time you modify the different parameters. A larger confidence level requires a larger observed difference. Enter which confidence level you want in order to prove that the observed difference is not by chance.Enter the sample size and the observed % for the sample 1.You have more info in the following post. How can you determine if this difference was by chance or advertisement A was really better? By doing a test of significance. You show each advertisement to 1.000 people and A advertisement gets 10,5% of sales conversion and B gets 11%. For example, you have two online advertisements to engage users in your web, A and B. Significant difference calculator between proportionsĬheck if the observed difference between two samples is significant regarding a percentage or a proportion.

Statistical calculations calcualtor