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Special Considerations for Conducting Research in Mission-Simulation Analog Environments
Published in Lauren Blackwell Landon, Kelley J. Slack, Eduardo Salas, Psychology and Human Performance in Space Programs, 2020
Suzanne T. Bell, Peter G. Roma, Bryan J. Caldwell
Button et al. (2013) provide a detailed discussion and demonstration of the consequences of low power for replicability. As they indicate, low-powered studies are more likely to produce more false negatives; they have a lower chance of finding a true effect when one exists in the population. Lower power also lowers the positive predictive value (PPV), or the probability that a positive research finding reflects a true effect. A lower-powered study also makes it less likely that an observed effect that passes a significance threshold (e.g., P < .05) reflects a true effect. Finally, even when an underpowered study discovers a true effect, the estimate of the magnitude of the effect is likely to be exaggerated, especially when the effect is newly discovered, called the ‘winner’s circle’ (Ioannidis, 2008). For these reasons, inferential statistics and the NHST framework are largely inappropriate in small sample size research. As such, other approaches must be used to provide evidence that an observed effect is likely to occur in the target population.
Artificial Intelligence Basics
Published in Subasish Das, Artificial Intelligence in Highway Safety, 2023
Positive predictive value (PPV): The PPV is the likelihood of the correct classification of a positively classified observation. It can be determined from the entries of the confusion matrix: PPV=Pr(Y−=1|Y=1)=TPTP+FP
Face, Fingerprint, and Signature based Multimodal Biometric System using Score Level and Decision Level Fusion Approaches
Published in IETE Journal of Research, 2023
Majharoddin Kazi, Karbhari Kale, Raddam Sami Mehsen, Arjun Mane, Vikas Humbe, Yogesh Rode, Siddharth Dabhade, Nagsen Bansod, Arshad Razvi, Prapti Deshmukh
where FN = False Negative, P = Positive. Miss Rate. It is the same as FNR.TNR = True Negative Rate.Specificity. It is the same as True Negative Rate (TNR).Positive Predictive Value (PPV).Estimated as where TP = True Positive and FP = False Positive. Precision (P). It is the same as Positive Predictive Value (PPV).Negative Predictive Value (NPV).Estimated as F-Score: It is also termed an F-measure. It is the weighted harmonic mean of precision (P) and recall (R).
VGI and crowdsourced data credibility analysis using spam email detection techniques
Published in International Journal of Digital Earth, 2018
Saman Koswatte, Kevin McDougall, Xiaoye Liu
A number of measures such as accuracy, precision, sensitivity and the F1-score provided an indication of each classification’s effectiveness. The accuracy, which is the ratio of correctly predicted observations, was calculated by the formula (TP + TN)/(TP + TN + FP + FN). The precision or positive predictive value (PPV) is the ratio of correct positive observations. The PPV was calculated by TP/(TP + FP). The F1-score (F1) is used to measure classification performance using the weighted recall and precision, where the recall is the percentage of relevant instances that are retrieved and was calculated by 2 * TP/(2 * TP + FP + FN). The sensitivity or true positive rate was calculated by TP/(TP + FN).