方差分析（英文）.ppt

Continental Access ANOVA Analysis of Variance Reminders Type I error: a true null hypothesis incorrectly rejected Variance:The variance is one of several indices of variability that statisticians use to characterize the dispersion among the measures in a given population. To calculate the variance of a given population, it is necessary to first calculate the mean of the scores, then measure the amount that each score deviates from the mean and then square that deviation (by multiplying it by itself). Numerically, the variance equals the average of the several squared deviations from the mean t-test:a measure on a random sample (or pair of samples) in which a mean (or pair of means) appears in the numerator and an estimate of the numerators standard deviation appears in the denominator based on the calculated s square or s squares of the samples. If these calculations yield a value of (t) that is sufficiently different from zero, the test is considered to be statistically significant Null hypothesis: depending on the data, the null hypothesis either will or will not be rejected as a viable possibility. The null hypothesis is often the reverse of what the experimenter actually believes; it is put forward to allow the data to contradict it. If the test is significant, the null hypothesis is rejected. Reminders The Central Limit Theorem consists of three statements: The mean of the sampling distribution of means is equal to the mean of the population from which the samples were drawn. The variance of the sampling distribution of means is equal to the variance of the population from which the samples were drawn divided by the size of the samples. If the original population is distributed normally (i.e. it is bell shaped), the sampling distribution of means will also be normal. If the original population is not normally distributed, the sampling distribution of means will increasingly approximate a normal distribution as sample size increases. (i.e. whe