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Related Concept Videos

Types of Limits I01:23

Types of Limits I

Limits are a key mathematical concept for understanding how functions behave as their input approaches specific values, particularly when the function is undefined. They help reveal trends and discontinuities by examining the values a function approaches rather than its actual value.One-sided limits focus on the direction from which a value is approached. When a function behaves differently depending on whether the input approaches from the left or the right, the two one-sided limits may not...
The Squeeze Theorem01:30

The Squeeze Theorem

Certain mathematical functions exhibit unpredictable or highly variable behavior near specific input values, making direct evaluation of their limits challenging. This complexity may arise from rapid oscillations or irregular patterns that obscure the function’s trend. In such cases, the Squeeze Theorem offers a reliable method for determining limits.According to the Squeeze Theorem, if a function is confined between two other functions near a particular point, and both outer functions approach...
Types of Limits II01:24

Types of Limits II

When observing how a curve behaves near a specific point along the horizontal axis, there are cases where the curve’s height increases or decreases without limit as the position draws closer to that point. The curve does not settle at any particular value; instead, the values grow more extreme—upward or downward—the nearer they get. No defined value exists exactly at that location, yet the surrounding behavior becomes more dramatic, indicating a sharp change in direction.The values may rise...
Prediction Intervals01:03

Prediction Intervals

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. 
The...
Naturalistic Observations02:30

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
Confidence Intervals01:21

Confidence Intervals

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 confidence...

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Related Experiment Videos

Inference limits in partially observable Ethereum blockchains.

Muhammad Zeshan Arshad1, Ali Algrani2

  • 1Faculty of Mathematics and Statistics, Brock University, St. Catharines, ON, L2S 3A1, Canada. vf_mzeshanarsha@brocku.ca.

Scientific Reports
|June 2, 2026
PubMed
Summary

Public blockchains present limited data access due to technical constraints, impacting blockchain analysis. This study reveals that partial observability significantly distorts empirical findings, highlighting data incompleteness as a critical issue.

Keywords:
BlockchainEthereumMissing dataObservabilitySimulation

Related Experiment Videos

Area of Science:

  • Computer Science
  • Data Science
  • Blockchain Technology

Background:

  • Full ledger access on public blockchains is often impractical due to storage, client design, indexing, and off-chain data limitations.
  • Empirical blockchain analysis typically relies on observable data projections rather than complete ledger data.

Purpose of the Study:

  • To reframe blockchain observability as an inferential problem with incomplete observations.
  • To analyze identifiability, information loss, and uncertainty under restricted data access.
  • To evaluate different visibility regimes and their impact on data analysis.

Main Methods:

  • Developed a framework defining full ledger, observable ledger, and observability mechanisms.
  • Assessed three visibility regimes: independent Bernoulli, clustered, and activity-dependent.
  • Conducted an empirical study using Ethereum block data (blocks 18,000,000-18,001,000) via Google BigQuery.
  • Simulated controlled missingness (MCAR, MAR, MNAR) to assess impact on inference.

Main Results:

  • Reduced visibility across all regimes led to increased uncertainty, RMSE, variance, and MSE.
  • The most significant data quality deterioration occurred when visibility depended on ledger conditions.
  • Increasing missingness amplified RMSE and bias in trend estimates.
  • The type of data incompleteness (MCAR, MAR, MNAR) significantly affected distortion levels.

Conclusions:

  • Partial observability is not merely a data quality footnote but a fundamental challenge in blockchain analysis.
  • Incomplete data access can substantially skew inference, particularly for block-level summaries on platforms like Ethereum.
  • Understanding and accounting for data visibility limitations are crucial for accurate blockchain research.