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Related Concept Videos

Significant Figures in Calculations00:58

Significant Figures in Calculations

Uncertainty in measurements can be avoided by reporting the results of a calculation with the correct number of significant figures. This can be determined by the following rules for rounding numbers:
Numerical Calculations01:24

Numerical Calculations

In engineering applications, the representation of the numerical value is critical. Presenting or reporting the answer is one of the essential parts of engineering practices. Numerical calculations are performed using handheld calculators or computers since numerically accurate answers are always preferred.
The solution to a problem is obtained using different methods. While manually solving algebraic symbols is one of the most common methods, the graphical method is often preferred. Computers...
Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Rules for Significant Figures01:44

Rules for Significant Figures

In any measurement, the precision of the measuring tool is an essential factor. An ordinary ruler, for example, can measure length to the closest millimeter; a caliper, on the other hand, can measure length to the nearest 0.01 mm. As a result, the caliper is a more precise measurement tool because it can measure extremely minute changes in length. The measurements will be more accurate if the measuring tool is more precise.
It should be emphasized that when we represent measured values, the...
Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...

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Related Experiment Video

Updated: Jun 21, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Rounding strategies for multiply imputed binary data.

Hakan Demirtas1

  • 1Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL 60130, USA. demirtas@uic.edu

Biometrical Journal. Biometrische Zeitschrift
|August 4, 2009
PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) for mixed data types can be challenging. This study compares rounding rules for binary variables, finding that methods borrowing information from other variables perform best.

Related Experiment Videos

Last Updated: Jun 21, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Multiple imputation (MI) is a common technique for handling incomplete datasets.
  • Standard imputation models exist for continuous or discrete data, but mixed data types pose challenges.
  • Treating discrete variables as continuous and rounding is a pragmatic approach in software packages.

Purpose of the Study:

  • To compare the performance of different rounding rules for binary variables in multiple imputation.
  • To evaluate the bias and coverage of estimates under various rounding strategies.
  • To provide guidance on best practices for handling mixed data types in imputation.

Main Methods:

  • Simulated longitudinal datasets with mixed continuous and discrete variables were used.
  • Conditional and marginal data generation mechanisms and imputation models were employed.
  • Statistical properties of MI-based estimates were assessed across different rounding rules.

Main Results:

  • The performance of rounding rules varied significantly.
  • Rules that leverage information from other variables in the dataset outperformed those relying solely on marginal characteristics.
  • Some rounding rules showed sensitivity to imputation model specifications.

Conclusions:

  • Effective rounding rules for binary variables in MI should utilize information from the entire variable system.
  • Researchers should carefully consider the applied context and avoid uncritical use of rounding in imputation models.
  • The choice of rounding rule can impact the validity of statistical estimates from incomplete datasets.