Related Experiment Video
Updated: Aug 18, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Statistical analysis of data in support of the Barker hypothesis, advantages of using random effects regression model
C Osmond1, W Y Kwong, T P Fleming
1MRC Environmental Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK.
Abstract:
In a recent note by Walters and Edwards (2004), the authors argued that summary statistics should be used in analysing hierarchical data from our earlier analysis of a rat model of developmental programming and the Barker hypothesis (Kwong et al., 2000, 2004). We reiterate here why such a view is inappropriate. Hierarchical data merits multilevel analysis using a 'random effects' model to enable estimation of variances at different levels and easy assessment of other parameters in a complex data structure.
Related Concept Videos
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Comparing the Survival Analysis of Two or More Groups
Friedman Two-way Analysis of Variance by Ranks
Statistical Methods to Analyze Parametric Data: ANOVA
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
One-Way ANOVA
One-Way ANOVA: Equal Sample Sizes
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...