Inferential statistics are used to make judgments about the probability that an observed difference between groups is a dependable one or one that might happen by chance. In this study with inferential statistics, one concludes that extend beyond the immediate data alone. Thus, one uses inferential statistics to make inferences from our data to more general conditions. Perhaps one of the simplest inferential tests is used when one has to compare the average performance of two groups on a single measure to see if there is a difference. Whenever one wishes to compare the average performance between two groups on should consider the t-test for difference between groups.
Most of the major inferential statistics come from a general family of statistical models known as the general linear model. This includes the t-test, analysis of variance (ANOVA), analysis of covariance
(ANCOVA), regression analysis, and many of the multivariate methods like factor analysis, population parameters from observing the sample values.
Inferential statistics provide a way of -
going from a " sample" to a "population"
inferring the "parameters" of a population from data on the "statistics" of a sample.
i.e., parameters like
from statics like m and s. But before we can see what is involved in the move from sample to population we need to understand yow to move from population to sample.
The study of obtaining a sample from the population is "probability".
Probability -
For Example - the probability of picking a black ball from jar A is one half; the probability of picking a black ball from jar B is one-tenth.
Most of the major inferential statistics come from a general family of statistical models known as the general linear model. This includes the t-test, analysis of variance (ANOVA), analysis of covariance
(ANCOVA), regression analysis, and many of the multivariate methods like factor analysis, population parameters from observing the sample values.
Inferential statistics provide a way of -
going from a " sample" to a "population"
inferring the "parameters" of a population from data on the "statistics" of a sample.
i.e., parameters like
from statics like m and s. But before we can see what is involved in the move from sample to population we need to understand yow to move from population to sample.
The study of obtaining a sample from the population is "probability".
Probability -
For Example - the probability of picking a black ball from jar A is one half; the probability of picking a black ball from jar B is one-tenth.
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