零假设(原假设)与备择假设

Irrelevant variables and their minimization
Three ways to control subject variables: 1. Repeated measures design; 2. Matched subjects design; 3. Independent groups design: 4. the most flexible and most widely used 5. by using a stratified 6. randomizing instead of simple
In linguistics and other socially oriented sciences, where measurement is often less
exact, a level of p(α)≤0.05 is quite common.
Significance levels
For any particular test, and any selected
Strategy of hypothesis testing
It is extremely important to realize that we can never prove conclusively that the null hypothesis is incorrect, or that any alternative hypothesis is correct. There is always a chance that the differences we observe are indeed due to sampling variation and not to the independent variable. All we can do is try to show that the probability of this being so is very small. But how small?
significance level, there will be a critical value (临界值)of the test statistic, which delimits a critical region (临界区域) within which the
Outline
1. Introduction 2. The design of investigations 3. a. Experimental and observational 4. studies 5. b. Irrelevant variables and their 6. minimization
Irrelevant variables and their minimization
Two types of irrelevant variables: Subject variables concerned with the properties of the experimental subjects themselves; Situational variables concerned with the conditions under which the experiment is carried out.
Experimental and observational studies
Experimental studies are extremely common in the natural and physical sciences and are also suitable for some types of linguistic investigation. Often, however, the linguist, psychologist or
It is symbolized as:
H0 : μA = μB
Null and alternative hypotheses
零假设（原假设）与备择假设
Alternative hypothesis: There is a difference between the values of a parameter in the populations from which the samples were drawn.
Significance levels
The probability level below which we are willing to treat our observed differences as
significant is called the significant level of a test.
Hypothesis Testins of significance
The aim of statistical tests of significance is to show whether or not the observed differences between sets of data could reasonably have been expected to occur ‘by chance’(that is, owing to sampling variation) or whether, on the contrary, they are most probably due to the alteration in the variable whose effect is being investigated. So we shall be more precise about what we mean by ‘reasonably’ and ‘most probably’ .
4. Summary of procedure for hypothesis testing
Introduction
So far, we have concentrated on the methods available for summarizing and describing data, and for estimating population parameters from sample statistics. We have said very little about the use of statistical techniques for testing hypotheses about the differences between sets of data.
3. It should be testified.
Null and alternative hypotheses
零假设（原假设）与备择假设
Null hypothesis: There is no difference between the values of a parameter in the populations from which the samples were drawn; hence the term ‘null’.
Properties of hypothesis
1. It should reveal the relationships among two or more variables;
2. It should be expressed in the form of questions or declarative sentences, with clear and accurate diction;
Experimental and observational studies
Experimental studies are those in which the investigator deliberately manipulates some factor(s) or circumstance(s) in order to test the effect on some other phenomenon.
Outline
3. Hypothesis testing a. Need for statistical tests of significance b. Properties of hypothesis c. Null and alternative hypotheses d. Strategy of hypothesis testing
Experimental and observational studies
sociologist is interested in areas where he cannot deliberately manipulate the independent variable. Studies of this kind are called observational or correlational studies.
Strategy of hypothesis testing
What we must do is to calculate, by means
of some suitable procedure, a test statistic
(检验统计值）which will allow us to find the probability of obtaining the results we have observed, on the assumption that the null hypothesis is true.
Outline
3. Hypothesis testing e. Significance levels f. Two types of error in significance testing g. One-tailed and two-tailed tests
Outline
3. Hypothesis testing h. Choosing a test (parametric tests and non-parametric tests)
It is symbolized as: H1 : μA = μB
H1 : μA > μB
H1 : μA < μB
Strategy of hypothesis testing
The strategy of hypothesis testing is to try to accumulate enough evidence to reject the null hypothesis, rather than to try to support any of the possible alternative hypotheses directly. This is also called ‘the method of disproof ’.
So in this chapter, the basic principles underlying statistical testing are examined. And we begin with the question of project design.
The design of Investigations
randomizing
Irrelevant variables and their minimization
The control of situational variables may be achieved in part by attempting to hold such variables constant, or making sure that they are experienced equally by all subjects.