Презентация на тему Hypothesis testing

its probability distribution

reflects how much sample evidence we require to reject

the null. Analogous to its counterpart in a court of law, the required standard of proof can change according to the nature of the hypotheses and the seriousness of the consequences of making a mistake. There are four possible outcomes when we test a null hypothesis:




The probability of a Type I error in testing a hypothesis is denoted by the Greek letter alpha, α. This probability is also known as the level of significance of the test.

of determining the rejection points (critical values) with which

to compare the test statistic to decide whether to reject or not to reject the null hypothesis. When we reject the null hypothesis, the result is said to be statistically significant.

statistic
Collecting the data by sampling the population


To reject or

not



The first six steps are the statistical steps. The final decision concerns our use of the statistical decision.
The economic or investment decision takes into consideration not only the statistical decision but also all pertinent economic issues.

6th: Making the statistical decision

7th: Making the economic or investment decision

at which the null hypothesis can be rejected. The

smaller the p-value, the stronger the evidence against the null hypothesis and in favor of the alternative hypothesis. The p-value approach to hypothesis testing does not involve setting a significance level; rather it involves computing a p-value for the test statistic and allowing the consumer of the research to interpret its significance.

the population mean of a normally distributed population with

unknown (known) variance, the theoretically correct test statistic is the t-statistic (z-statistic). In the unknown variance case, given large samples (generally, samples of 30 or more observations), the z-statistic may be used in place of the t-statistic because of the force of the central limit theorem.
The t-distribution is a symmetrical distribution defined by a single parameter: degrees of freedom. Compared to the standard normal distribution, the t-distribution has fatter tails.

test whether the observed difference between two means is

statistically significant, we must first decide whether the samples are independent or dependent (related). If the samples are independent, we conduct tests concerning differences between means. If the samples are dependent, we conduct tests of mean differences (paired comparisons tests).
When we conduct a test of the difference between two population means from normally distributed populations with unknown variances, if we can assume the variances are equal, we use a t-test based on pooling the observations of the two samples to estimate the common (but unknown) variance. This test is based on an assumption of independent samples.

based on two samples that are not independent, we

often can arrange the data in paired observations and conduct a test of mean differences (a paired comparisons test). When the samples are from normally distributed populations with unknown variances, the appropriate test statistic is a t-statistic. The denominator of the t-statistic, the standard error of the mean differences, takes account of correlation between the samples.

variance of a single, normally distributed population, the test

statistic is chi-square (χ2) with n − 1 degrees of freedom, where n is sample size.

Variance.

tests concerning differences between the variances of two normally

distributed populations based on two random, independent samples, the appropriate test statistic is based on an F-test (the ratio of the sample variances).

concerning a parameter or a hypothesis test based on

specific distributional assumptions. In contrast, a nonparametric test either is not concerned with a parameter or makes minimal assumptions about the population from which the sample comes.
A nonparametric test is primarily used in three situations: when data do not meet distributional assumptions, when data are given in ranks, or when the hypothesis we are addressing does not concern a parameter.

rank correlation coefficient is essentially equivalent to the usual

correlation coefficient calculated on the ranks of the two variables (say X and Y) within their respective samples. Thus it is a number between −1 and +1, where −1 (+1) denotes a perfect inverse (positive) straight-line relationship between the variables and 0 represents the absence of any straight-line relationship (no correlation). The calculation of rS requires the following steps: