PHƯƠNG PHÁP THỐNG KÊ KHOA HỌC MÔI TRƯỜNG Đo lường tất cả các liên quan đến lỗi. Bất kỳ lĩnh vực sử dụng các phương pháp thực nghiệm do đó phải được quan tâm về biến đổi trong dữ liệu của nó. Đôi khi mối quan tâm này có thể hạn chế sai sót về đo lường trực tiếp. Các nhà vật lý người muốn xác định tốc độ của ánh sáng đang tìm kiếm xấp xỉ tốt nhất cho một hằng số được giả định là có một giá trị cố định, duy nhất đúng. Thường xuyên hơn rất nhiều,. | STATISTICAL METHODS FOR ENVIRONMENTAL SCIENCE All measurement involves error. Any field which uses empirical methods must therefore be concerned about variability in its data. Sometimes this concern may be limited to errors of direct measurement. The physicist who wishes to determine the speed of light is looking for the best approximation to a constant which is assumed to have a single fixed true value. Far more often however the investigator views his data as samples from a larger population to which he wishes to apply his results. The scientist who analyzes water samples from a lake is concerned with more than the accuracy of the tests he makes upon his samples. Equally crucial is the extent to which these samples are representative of the lake from which they were drawn. Problems of inference from sampled data to some more general population are omnipresent in the environmental field. A vast body of statistical theory and procedure has been developed to deal with such problems. This paper will concentrate on the basic concepts which underlie the use of these procedures. FIGURE 1 DISTRIBUTIONS Discrete Distributions A fundamental concept in statistical analysis is the probability of an event. For any actual observation situation or experiment there are several possible observations or outcomes. The set of all possible outcomes is the sample space. Some outcomes may occur more often than others. The relative frequency of a given outcome is its probability a suitable set of probabilities associated with the points in a sample space yield a probability measure. A function x defined over a sample space with a probability measure is called a random variable and its distribution will be described by the probability measure. Many discrete probability distributions have been studied. Perhaps the more familiar of these is the binomial distribution. In this case there are only two possible events for example heads and tails in coin flipping. The probability of obtaining x
