tailieunhanh - Niche Modeling: Predictions From Statistical Distributions - Chapter 7

Thiên vị thường được hiểu như là một thành kiến theo ý nghĩa để có một thị hiếu điểm cụ thể xem hoặc quan điểm ý thức hệ. Thiên vị thống kê là một xu hướng định lượng của một ước tính thống kê đến hơn hoặc đánh giá thấp số lượng được ước tính. Về mặt chính thức, nếu θ là một ước tính của một tham số θ, sau đó θ là thành kiến nếu giá trị kỳ vọng của θ, E (θ), không bằng để θ. Bias là một mối quan tâm khi quá trình bất kỳ. | Chapter 7 Bias A bias is generally understood as a prejudice in the sense for having a predilection to one particular point of view or ideological perspective. Statistical bias is a quantifiable tendency of a statistical estimator to over or underestimate the quantity that is being estimated. In formal terms if e is an estimator of a parameter e then e is biased if the expected value of e E e is not equal to e. Bias is a concern when any process is supposed to be objective and fair. Similarly statistical bias of an estimator should be of concern as that bias can influence results and undermine the objectivity of conclusions. For example systematic errors increase the likelihood of assigning statistical significance to chance events Type I error . Given an hypothesis Ho e e and the alternative Ha e e then bias would tend to increase the chance of rejecting Ho when Ho is true or committing a Type I error. Bias is more insidious than uncertainty introduced by small samples. For example errors in estimation of a mean value are generally symmetric that is equally as likely to be above or below the actual value. Thus while a small sample introduces uncertainty it does not necessarily bias the result. The detection of bias in modeling should be a primary concern. While increasing the sample size can reduce uncertainty in symmetric errors averaging is not a solution to asymmetric errors. The variation in symmetric sampling errors reduces our power to detect statistical patterns in data Type II error . Type II errors only lead us to reserve judgment but Type I errors cause us to draw incorrect conclusions. In most cases drawing incorrect conclusions should be a greater concern than failing to find significance. 115 2007 by Taylor and Francis Group LLC 116 Niche Modeling Range shift We look at potential bias in the range-shift methodology. This methodology examines effects of potential shifts in the ranges of species that may be brought on by changes in the environment.