Tie Resolution
Tie Resolution - Related Videos
10:08
High-Resolution Respirometry in a Small-Volume Chamber
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Cited by 3 •
2025
Here, we show that the 0.5-mL chamber for high-resolution respirometry shows results that are consistent with the 2.0-mL chamber if appropriate instrumental O2 background correction is applied. It requires less sample, which is advantageous for studies with limited availability or low respiratory capacities.
08:31
Three-dimensional Optical-resolution Photoacoustic Microscopy
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Cited by 27 •
2011
Optical-resolution photoacoustic microscopy (OR-PAM) is an emerging technology capable of imaging optical absorption contrasts in vivo with cellular resolution and sensitivity. Here, we provide a visualized instruction on the experimental protocols of OR-PAM, including system configuration, system alignment, typical in vivo experimental procedures, and functional imaging schemes.
07:49
High-Resolution C. elegans Imaging Across All Larval Stages
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Cited by 3 •
2025
This protocol describes microfluidic-based C. elegans time-lapse imaging across the entire post-embryonic development.
01:15
Chromatographic Resolution
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2024
In chromatography, a solute moves through a chromatographic column and tends to spread, forming a Gaussian-shaped band. The longer the solute spends in the column, the broader the band becomes. The broadening can lead to overlaps within the column, affecting separation effectiveness. The effectiveness of separation can be evaluated by determining the level of separation between two neighboring peaks in a chromatogram, which represents the individual components of a sample. In chromatography,...
16:52
Test Samples for Optimizing STORM Super-Resolution Microscopy
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Cited by 46 •
2013
We describe the preparation of three test samples and how they can be used to optimize and assess the performance of STORM microscopes. Using these examples we show how to acquire raw data and then process it to acquire super-resolution images in cells of approximately 30-50 nm resolution.
FAQs
Average-rank assignment gives equal observations the same position while preserving their shared location in the ordered data. This avoids imposing an arbitrary distinction between values that are numerically equal. Analysts may instead choose competition-ranking rules or a consistent randomization procedure when those choices better match the analysis or study design.
Tied observations change the distribution of ranks and can also change the variance estimate used by a nonparametric test. Tie-correction factors account for this alteration in procedures such as the Wilcoxon, Mann–Whitney, and Kruskal–Wallis tests. Applying the correction supports more accurate test statistics and p-values than treating all ranks as distinct.
The appropriate rule depends on the analysis and study design. Average ranks treat equal observations as sharing their mean position, whereas competition-ranking rules preserve a different ordering convention. A consistent randomization procedure can be used when the design requires an ordered resolution without repeatedly making arbitrary choices. The selected rule should remain consistent throughout the analysis.
First, identify equal observations or tied ranks in the dataset. Next, select a resolution rule that fits the analysis and study design, such as average ranks, competition ranking, or consistent randomization. For nonparametric testing, account for the resulting rank distribution and variance changes with an appropriate tie-correction factor before interpreting the test statistic and p-value.
Tie handling is especially important for nonparametric procedures that rely on ranked observations, including the Wilcoxon, Mann–Whitney, and Kruskal–Wallis tests. In these methods, equal values can alter both the rank distribution and variance estimates. Analysts should therefore incorporate tie corrections when interpreting the resulting test statistics and p-values.
Tie resolution supports analyses of grouped, ordinal, and discrete data, where equal observations may occur frequently and forcing an arbitrary order could reduce interpretive validity. A clearly defined rule also improves reproducibility because different analysts can apply the same treatment to identical data. This consistency helps researchers compare results and interpret rank-based findings more reliably.