假设检验与参数英文概念解释汇集

1、Hypothesis testingAverage could not satisfy our data analysis. Because there may exist error, and many reason would result the error. So we need hypothesis testing to judge whether the error would affect the result. Hypothesis test is the method that makes a hypothesis based on the probability distr

2、ibution or distribution parameter of population, according to the sample observations, makes use of statistical methods of analysis to test the correctness of this hypothesis, and decides to accept or reject the hypothesis. Hypothesis test also is called the test of significance. Hypothesis testing

3、is another important content in statistics. There are many methods in hypothesis testing, such as t-inspection, F-inspection,-inspection and so on. Hypothesis testing can judge the different between estimated value and general value with a little risk. It estimated the total by the sample, and the r

4、esult is reliable entirely. Besides, the purpose of inspection is to analyse if evident contrast between t sample index and total index, instead of doubt computed result of sample index itself. From this meaning, hypothesis testing can also named significance test.Hypothesis testing is a statistical

5、 inference method, which is used to determine whether the difference between sample and sample or sample and population caused by sampling error or essential difference. The basic principle of hypothesis testing is to make certain hypothesis for the character of population, and then reject or accept

6、 this hypothesis through inferences reasoning. And there are three basic steps in hypothesis testing that are: set up hypothesis and determine the size of a test, while, the hypothesis contains null hypothesis （H0）and alternative hypothesis (HA). The meaning of null hypothesis is that the test surfa

7、ce difference caused by the sampling error, while, the alternative hypothesis shows that the test surface difference also contains test real difference besides sampling error. select test method and calculate statistics; determine the P value and then infer the conclusion according to the principle

8、of little probability. At the same time, if negative region (H0) locates the two tail of the sampling distribution curve, and the probability of left tail and right tail also are, then, this kind of hypothesis testing is called two-tailed test. While, if negative region (H0) only locates one tile of

9、 the sampling distribution curve, and its probability is, and then describe this kind of hypothesis testing as one-tailed test. In addition, if the analysis aims to infer that there is no difference between the two treatment effects, then use the two-tailed test, while, if the analysis aims to infer

10、 whether A treatment effect better than B treatment effect, then use one-tailed effect.A statistical hypothesis test is a method of making decisions using experimental data. In statistics, a result is called statistically significant if it is unlikely to have occurred by chance. Hypothesis testing i

11、s sometimes called confirmatory data analysis, in contrast to exploratory data analysis. In frequency probability, these decisions are almost always made using null-hypothesis tests. One use of hypothesis testing is deciding whether experimental results contain enough information to cast doubt on co

12、nventional wisdom. Through hypothesis testing, on the one hand we have the basis of a sample mean difference between 1 and 2 to infer the overall average of 1 and 2 are the same, on the other hand can not only according to the sample mean difference between 1 and 2 directly to the overall mean 1 and

13、 2 are the same conclusion, the reason lies in the inevitability of pilot error.The critical region of a hypothesis test is the set of all outcomes which, if they occur, will lead us to decide that there is a difference. That is, cause the null hypothesis to be rejected in favor of the alternative h

14、ypothesis. Significance of hypothesis testing Hypothesis testing is based on the original data to make a general indicator whether equal to certain a value, certain a random variable whether equal to obey hypothesis of certain probability distribution. Then use the sample data and to calculate some

15、relevant test statistic by statistical methods. Based on the probability principle, the smaller risk to judge whether the estimated value and the overall values were significantly different, whether to accept the original hypothesis of testing methods. Parametric test parameter test is statistical t

16、est that carried out for parameters mean, variance. The measured sample data to calculated test statistics. If the calculated statistical value into the rejection region that in the significant level of agreement , describe that between the seized parameters have significant difference under the sig

17、nificant level in statistics. Otherwise, If the calculated statistical value into the receptive fields that in the significant level of agreement . describe that between the seized parameters havent significant difference, it is the parameter estimates of the same total value.1.2 Significance testFi

18、rstly, make out a hypothesis for the totals parameter or the shape of the total, then make use of the sample information to judge whether the null hypothesis is right or not. That is to say, we need to judge out if there has evident distinction between reality and null hypothesis. Significance test

19、is put forward on account of general assumption we gain it. According to the small probability event, the assumption will be accept or repudiate, this is also the principle of significance test.Null hypothesis and alternative hypothesisH0 : d =0 ，HA : d 0d is the average of two samples difference. I

20、t also equal to the difference between 1 and 2. So the null hypothesis and alternative hypothesis means H0 : 1 =2 and HA : 12 Null hypothesis is to propose a hypothetical goal for overall study. The null means there is no real difference between the treatment effect and the assumed value, obtained d

21、ifferences in the test results are errors. Null hypothesis is the hypothesis to be tested and may be negative, may not be denied by testing. Null hypothesis put forward and a corresponding hypothesis put forward, called the alternative hypothesis. Alternative hypothesis is prepared to accept when th

22、e null hypothesis is denied. That is, the surface difference of the test is not only includes testing errors, but also contains a test real difference.Parametric and non-parametric dataParametric needs sample which was from the known population. And base on this kind of suppose, deduce the total par

23、ametric.Non-parametric data is a kind which doesnt depend on the population. It also doesnt base on this kind of suppose, deduce the total parametric.Procedure of hypothesis test (1) Make the hypothesis of the sample for the populationMake the null hypothesis H0：1=2 or 12=0. Correspondingly, the alt

24、ernative hypothesis is given-HA: 12. (2) In the premise of null hypothesis forming, use the correct method to calculate the probability when the null hypothesis is correct.Usually the t test is used to calculate the probability. (3) Make statistical inferenceAccording to the t value and df, make sta

25、tistical inference based on attachment table 3, and find the t0.05，t0.01。In the biological study, based on the small probability of principle, the probability of null hypothesis is called the significance level. =0.05 is the 5% significance level and =0.01 is the 1% significance level. If |t| 0.05,

26、we should accept H0：1=2.If t0.05 |t| t0.01, then 0.01P0.05, we should accept HA: 12. Parametric and nonparametric hypothesis test Hypothesis test can divide into two types: parametric hypothesis test and non-hypothesis hypothesis test.Parametric hypothesis test 2Parameter hypothesis test is the hypo

27、thesis test about the unknown parameters knowing the distribution form of population.Parametric statistics is a branch of statistics that assumes data come from a type of probability distribution and makes inferences about the parameters of the distribution. Most well-known elementary statistical me

28、thods are parametric. If those extra assumptions are correct, parametric methods can produce more accurate and precise estimates. They are said to have more statistical power. Nonparametric hypothesis testNon-parametric hypothesis test, also known as non-distribution hypothesis test, is the general

29、one about population in the unknown mathematical form of population distribution.Non-parametric statistic can refer to a statistic whose interpretation does not depend on the population fitting any parametrized distributions. Statistics based on the ranks of observations are one example of such stat

30、istics and these play a central role in many non-parametric approaches. Two-tailed and one-tailed test 2Under level about the negative domain of (, t and t, ), symmetrically located on the left and right sides of the horizontal tail, the probability of each side of the tail is / 2, that is, P (t=t)

31、= P (tt= +). This is the two-tailed test or two-sided test. Figure 1 Two-tailed testUnder level about the negative domain of t, ), the probability of right side of the tail is , that is, P (tt= +) = . Under level about the negative domain of (, t, the probability of left side of the tail is , that i

32、s, P (t=t) = . This test is called one-tailed test or one-sided test. Figure 2 One-tailed test Paired design and non-paired design 3Non-paired designWhen only two treatment means test carried out when the test units will be completely randomly divided into two groups, and then put a handle on the tw

33、o groups randomly. In this design two pilot units independent of each other, from the two samples are independent, its content does not necessarily equal. Unpaired data experimental design refers to when progress the test that has two treatments, the test units were randomly divided into two groups,

34、 then implement one treatment for each group. In this design, not only the test units of the two groups but also the two samples are independent, in addition, the content of the samples are not necessarily equal. Non-paired design refers to that the test variations are divided into two groups random

35、ly and deal with them. The size of samples isnt equal surely because of their independence. Procedure (1) Make null hypothesis and alternative hypothesis H0：1=2 or 12=0, HA: 12. (2) Calculate t value t=,And df= (n1-1)+ (n2-1) (When n1= n2, df= n-1)(3) Make statistical inference According to the t va

36、lue and df, make statistical inference based on attachment table 3, and find the t0.05，t0.01, and compare with them. Paired designRefers to the first request under the matching pair test unit 22, then the two test units paired pairs randomly assigned to two treatment group. Matching requirem

37、ent is paired pairs of initial conditions the two pilot units to be consistent between different pairs test unit allowed different initial conditions, each pair is a repeated treatments. Paired two ways: their pairing and homologous pairing.Paired data experimental design means according to the matc

38、hing requirements to pair two test units, and then the two test units, which have paired pair, will randomly distribute into the two treatments. Such as:In order to eliminate the differences and improve the accuracy, paired design is used to study. According to meet the requirements of the test unit

39、 to match each other, they are randomly assigned to two groups. The Premise is to ensure the initial conditions of two experimental units consistent.Procedure (1) Make null hypothesis and alternative hypothesis H0：1=2 or 12=0, HA: 12. (2) Calculate t value, df = (n-1)And (3) Make statistical inferen

40、ce According to the t value and df, make statistical inference based on attachment table 3, and find the t0.05，t0.01, and compare with them. 2.1.3 Parametric significance tests Invalid assumptions used to determine the probability of being denied standard called significance level. In biological res

41、earch often take 0.05 or 0.01, respectively, called the 5% significance level and 1% significant level (significant level).Parameter is one of the variable, which be use to control other values as change as parameter will. On the statistics, it is a recapitulative value to depict the totals characte

42、ristic. And nonparametric is relative with parameter, it also used the simple mark (+ and -), or two number (0 and 1 often be used) to instead of the concrete value. Therefore, the method to make used of parameter to validate results will be apply on this date.1.3.2 Parameter test -null hypothesis a

43、nd alternative hypothesisNull hypothesis often put forward a predicted objective for the study total. This is no effect if there havent real difference between treatment effect and assumed datum, may be some error will lead to the discrepant result. Usually mark it as . Then, alternative hypothesis is corresponding to null hy