China
Africa
Explore chapters and articles related to this topic
Clause 7: Support
Published in Sid Ahmed Benraouane, H. James Harrington, Using the ISO 56002 Innovation Management System, 2021
Sid. Ahmed Benraouane, H. James Harrington
You can also use other machine learning-based applications to analyze data and train models at making predictions. When using AI-based applications to train your models at predictions, it is critically important that you pay attention to common errors that could lead to an erroneous interpretation of the data. Common data errors to avoid are:Overfitting: The error of forcing an explanation into a dataset.Sampling bias: The error in which your selection of the sample did not account for the entire population you are supposed to include.False causality: The error in which you mistakenly link a cause and an effect.Confirmation bias: The error in which you seek information and data that confirm your initial belief about a trend or an event.Unconscious or implicit bias: The error in which you unfairly choose a course of action like choosing to invest in technology XY simply because you happen to like this technology.Anchoring bias: The error of overly focusing on one piece of information or data, with no evidence that this piece of data has any relevance to the decision being made.
System Stability and Sustainability
Published in R. S. Bridger, Introduction to Human Factors and Ergonomics, 2017
There are several sources of bias in ergonomic surveys. Sampling bias can occur if the sample is not random and key groups are under- or overrepresented. This can be avoided by using stratified sampling techniques and large samples. Response bias can occur if key groups are less likely to respond to the survey. The risk can be minimized using the tactics provided in Table 16.3. It is sometimes possible to reduce response bias post hoc by “re-balancing” the sample of respondents. This involves randomly removing respondents from other demographic or user groups, so that the sample of respondents again mirrors the population of interest. Unfortunately, this wastes valuable data.
Image sampling techniques and requirements for automated image analysis of rock fragmentation
Published in John A. Franklin, Takis Katsabanis, Measurement of Blast Fragmentation, 2018
A universal problem in characterizing large populations with too many individuals to measure is that generalizations have to be made from a limited number of samples (Maerz 1990). In general the more samples there are, the closer the measured sample parameter will be to the true population parameter. If the sampling is not random or systematic, a sampling bias could result in a misleading sample parameter.
The categorization of amateur cyclists as research participants: findings from an observational study
Published in Journal of Sports Sciences, 2018
Jose Ignacio Priego Quesada, Zachary Y. Kerr, William M. Bertucci, Felipe P. Carpes
Sampling bias, originating from a sample not being representative of its intended target population, is of concern as it may limit the external validity of findings, affect the replicability of research protocols, augment types I and II statistical errors, and consequently affect the applicability of the results (Cooke & Jones, 2017; Shephard, 1998). Furthermore, an appropriate sample selection helps to ensure that the comparability among studies from the same target population can be maximized. Within the cycling population, there is a wide range of characteristics related to experience, fitness level, riding purpose, and cycling discipline, all of which can affect different parameters such as pedaling kinematics (Bini et al., 2016; Bini, Hume, & Kilding, 2014), patterns of muscle recruitment (Chapman, Vicenzino, Blanch, & Hodges, 2007, 2009), pedal forces (Candotti et al., 2007; García-López, Díez-Leal, Ogueta-Alday, Larrazabal, & Rodríguez-Marroyo, 2016) and physiological outcomes (Coyle et al., 1991; Lee, Martin, Anson, Grundy, & Hahn, 2002). Thus, it is imperative for researchers to carefully consider the characteristics of cyclists that they recruit for their studies.
The Effects of Organizational Structure on MBSE Adoption in Industry: Insights from Practitioners
Published in Engineering Management Journal, 2023
Kaitlin Henderson, Alejandro Salado
Four areas that are threats to validity for surveys will be discussed for the rest of this section. Three are common to all surveys: sampling bias, instrumentation bias, and non-response bias. One is specific to surveys about organizations, which is capturing the emergent properties of an organization using an individual survey response.