Exam 17: Hypothesis Testing: Basic Concepts and Tests of Association

If the p-value is less than or equal to 0.05, then it is valid to say that the sample evidence is significant at .05 level.

The larger the degrees of freedom, the lower is the likelihood of observing differences among the variables.

The Chi square test can provide a useful measurement of association.

The Chi square test cannot be used to ascertain whether the observed pattern fits with the expected pattern.

A p-value of .45 means that the evidence against the null hypothesis is very weak.

Hypothesis testing can be used to establish whether the null hypothesis is true or false.

Type II error occurs when the null hypothesis is not rejected when it is false.

A high value of b indicates that the test of hypothesis is working very well.

The number of degrees of freedom, v, for the chi‐square test of independence is obtained using the formula v = r -1) * c -1).

Assuming the hypothesis to be true, the significance level indicates the percentage of sample means that are outside the cutoff limits.

Two branches of a major multinational corporation conducted surveys to measure the association between income level low, mid, high) and need for a certain produce low, mid, high).The sample size in one survey was 100 and in the other, 200.The branches now wish to compare the two cross tabulations that were generated from the surveys.Given this information, the Chi square statistic would provide an easy-to-interpret method to compare the associations.

The process of hypothesis testing, begins with an assumption about a sample statistic

A high p-value means that the probability of a statistically significant difference is high.

A p-value of .005 proves that the null hypothesis is false.

The contingency coefficient varies between 0 and 1.The 0 value occurs in the case of no association i.e., the variables are statistically independent), but the maximum value of 1 is never achieved.

The null hypothesis associated with the sample Chi square statistic is that the two intervally scaled) variables are statistically independent.

In order to reduce the probability of committing a Type II error, the probability of committing a Type I error must necessarily decreased

The higher the significance level used for testing a hypothesis, the higher is the probability of rejecting the null hypothesis when it is true.

The hypothesis test serves to quantify the reliability of research results, indicating the extent to which the data support the empirical findings.

Degrees of freedom refers to the number or bits of "free" or unconstrained data used in calculating a sample statistic.