Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Confidence Intervals01:21

Confidence Intervals

6.1K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
6.1K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

5.6K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
5.6K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.2K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.2K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

3.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
3.1K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

154
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
154
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same authorSame journal

Immediate level change estimates can be biased when interrupted time series analyses aggregate over time using segmented linear regression.

Journal of clinical epidemiology·2026
Same author

The effects of smoking and vaping on multiple sclerosis onset and disease progression: An umbrella review.

Multiple sclerosis and related disorders·2026
Same author

Enteral When Compared With IV Magnesium Replacement in the Critically Ill: A Noninferiority Randomized Clinical Trial.

Critical care medicine·2026
Same author

An investigation of discrepancies in outcome reporting and selective reporting bias in interrupted time series studies of health interventions: a methodological study.

BMC public health·2026
Same author

Optimal delivery of enteral protein in the critically ill: A systematic review and meta-analysis of randomised controlled trials.

Clinical nutrition (Edinburgh, Scotland)·2026
Same author

Psychosocial interventions for supporting women to stop smoking in pregnancy.

The Cochrane database of systematic reviews·2026

Related Experiment Video

Updated: Jun 8, 2025

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.4K

The Banksia plot: a method for visually comparing point estimates and confidence intervals across datasets.

Simon L Turner1, Amalia Karahalios2, Elizabeth Korevaar1

  • 1School of Public Health and Preventive Medicine, Monash University, Melbourne 3004, Victoria, Australia.

Journal of Clinical Epidemiology
|November 8, 2024
PubMed
Summary

The Banksia plot visually compares statistical analysis methods by standardizing point estimates and confidence intervals (CIs). This graphical tool simplifies identifying differences in results across various methods and datasets.

Keywords:
Banksia plotDiagramEmpirical studyGraphical representationIntercoder reliabilityPairwise comparisons

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Related Experiment Videos

Last Updated: Jun 8, 2025

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Area of Science:

  • Statistical analysis
  • Data visualization
  • Research methodology

Background:

  • Comparing statistical analysis methods is crucial but challenging when outcomes and scales vary.
  • Existing methods for comparing point estimates and confidence intervals (CIs) across different datasets can be difficult to interpret.
  • A need exists for a clear, visual method to facilitate these comparisons.

Purpose of the Study:

  • To introduce and illustrate the Banksia plot, a novel graphical method for comparing statistical analysis results.
  • To enable straightforward pairwise comparisons of point estimates and CIs from different analysis methods.
  • To facilitate comparisons both within and across datasets with differing outcome scales.

Main Methods:

  • The Banksia plot standardizes point estimates and confidence intervals (CIs) by centering them at zero and scaling them to a common range (-0.5 to 0.5).
  • It plots these adjusted values for a comparator method against a reference method, visualized on a standardized rectangle.
  • The method can be extended to a matrix of plots for multiple pairwise comparisons between several analysis methods.

Main Results:

  • The Banksia plot visually highlights differences in point estimates and CIs between various analysis methods.
  • It effectively reveals discrepancies when using different data extractors.
  • The plot allows for flexible ordering of CIs to emphasize specific differences, such as in point estimates or CI widths.

Conclusions:

  • The Banksia plot offers an intuitive visual summary for comparing multiple statistical analysis methods.
  • It simplifies the identification of patterns and trends in point estimates and CIs.
  • This graphical tool enhances the evaluation of statistical analysis method performance.