A more straightforward derivation of the LR for a database search

Charles E H Berger1, Peter Vergeer2, John S Buckleton3

  • 1Netherlands Forensic Institute, PO Box 24044, 2490 AA The Hague, The Netherlands; Leiden University, Institute for Criminal Law and Criminology, PO Box 9520, 2300 RA Leiden, The Netherlands.

Related Concept Videos

Odds Ratio01:09

Odds Ratio

The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
2.4K
Derivatives of Logarithmic Functions01:22

Derivatives of Logarithmic Functions

Logarithmic and Exponential RelationshipA logarithmic function is the inverse of an exponential function. If y = logb x then, it can be rewritten as by = x. This relationship allows for implicit differentiation, making logarithmic functions useful in calculus. Logarithmic scales are widely used to represent data that span multiple orders of magnitude, such as earthquake magnitudes (Richter scale) and sound intensity (decibels).Differentiation of Logarithmic FunctionsTo differentiate y = logb x,...
269
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
8.9K
Introduction to the Sign Test01:10

Introduction to the Sign Test

The sign test is an important tool in nonparametric statistics, offering a straightforward yet effective method for analyzing matched pairs, nominal data, or hypotheses concerning the median of a population. It transforms data points into positive or negative signs, avoiding the need for assumptions about data distribution and instead focusing on the direction of change. It is particularly valuable when data does not conform to the normal distribution requirements of many parametric tests. For...
1.5K
Probability Laws01:49

Probability Laws

Overview
45.4K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
486