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Prediction Intervals

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.

Endoscopic Procedures II: Colonoscopy

Endoscopic Procedures II: Colonoscopy

The colon, or large intestine, is the final segment of the digestive system. Its primary functions include absorbing water and vitamins produced by gut bacteria and transforming waste from liquid to solid to form stool. In adults, the large intestine is approximately 5 feet long and consists of four main sections:

Confidence Intervals

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...

Improper Integrals: Infinite Intervals

Improper Integrals: Infinite Intervals

An integral is classified as improper due to an infinite interval when at least one of its limits of integration extends to positive or negative infinity. In such cases, the region under the curve is unbounded, and standard techniques for evaluating definite integrals are not directly applicable. Instead, the improper integral is defined through a limiting process that allows one to determine whether the accumulated area remains finite despite the infinite domain.Application to Exponential...

Interval Level of Measurement

Interval Level of Measurement

For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...

Interpretation of Confidence Intervals

Interpretation of Confidence Intervals

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...

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Related Experiment Video

Updated: Feb 6, 2026

A Genetically Engineered Mouse Model of Sporadic Colorectal Cancer

A Genetically Engineered Mouse Model of Sporadic Colorectal Cancer

Published on: July 6, 2017

Interval colorectal cancer after colonoscopy.

C Teixeira¹, C Martins¹, E Dantas¹

¹Departamento de Gastroenterología, Centro Hospitalar de Setúbal, Setúbal, Portugal.

Revista De Gastroenterologia De Mexico (English)

|August 16, 2018

Summary

Interval colorectal cancer (iCRC) accounted for 3.76% of cases, with family history being a key differentiator. Further research is needed to understand iCRC development and improve screening strategies.

Keywords:

Colonoscopia Colonoscopy Cáncer colorrectal de intervalo Interval colorectal cancer

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Published on: October 16, 2013

Area of Science:

Gastroenterology
Oncology
Preventive Medicine

Background:

Interval colorectal cancer (iCRC) arises from missed lesions or new tumor development post-colonoscopy.
Understanding iCRC rates and characteristics is crucial for optimizing colorectal cancer (CRC) screening protocols.

Purpose of the Study:

To determine the incidence and clinical features of iCRC within a specific patient population.
To identify potential risk factors and explanations for iCRC development.

Main Methods:

A retrospective analysis of 266 colorectal cancer (CRC) cases diagnosed between January 2011 and January 2015.
Identification of iCRC cases among patients who underwent prior colonoscopy.
Comparison of demographic, endoscopic, and tumor characteristics between iCRC and non-iCRC patients.

Main Results:

Ten cases of iCRC were identified, representing 3.76% of all CRC cases.
Patients with iCRC showed a trend towards right colon tumors (50% vs. 24.5%).
A significantly higher proportion of iCRC patients had a family history of CRC (50% vs. 3.1%, P=.000).

Conclusions:

Family history of colorectal cancer is a significant distinguishing factor for interval colorectal cancer.
No other statistically significant differences were observed between iCRC and non-iCRC groups in this series.
These findings highlight the importance of family history in CRC screening and surveillance strategies.