Related Experiment Video
Updated: May 13, 2026

Muscle Imbalances: Testing and Training Functional Eccentric Hamstring Strength in Athletic Populations
Published on: May 1, 2018
Validity of Boston marathon qualifying times
1Dept of Health and Sport Sciences, University of Dayton, Dayton, OH.
Purpose:
To assess the validity of Boston Marathon qualifying (BMQ) standards for men and women.
Methods:
Percent differences between BMQ and current world records (WR) by sex and age group were computed. WR was chosen as the criterion comparison because it is not confounded by intensity, body composition, lifestyle, or environmental factors. A consistent difference across age groups would indicate an appropriate slope of the age-vs-BMQ curve. Inconsistent differences were corrected by adjusting BMQ standards to achieve a uniform percentage difference from WR.
Results:
BMQ standards for men were consistently ~50% slower than WR (mean 51.5% ± 1.4%, range 49.6-54.4%), thus demonstrating acceptable validity. However, BMQ standards for women indicated convergence with WR as age increased (mean 45.8% ± 13.7%, range 17.5-58.9%). The women's BMQ standards were revised to yield a consistent 50% deviation from WR across age groups (50.9% ± 0.8%, range 49.2-52.2%). Applied to all 16,773 women in the 2012 Chicago Marathon, the suggested BMQ standards would lead to a 4.90% success rate, compared with 8.39% using the current standard. This compared with a 9.6% success rate for all 20,681 men of the same race.
Conclusions:
The current women's BMQ standards appear too lenient for women 18-54 y and too strict for women 55-80 y but yield equitable gender representation in percentage of qualifiers. The current men's and suggested women's BMQ standards appear valid but would lead to approximately 40% fewer women achieving BMQ standards.
Related Concept Videos
Reliability and Validity
Construction of Frequency Distribution
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is best to...
Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Data Validation
Key parameters for method validation include:
Detection of Gross Error: The Q Test
Uncertainty in Measurement: Reading Instruments
