{"id":335,"date":"2019-08-02T05:30:07","date_gmt":"2019-08-02T05:30:07","guid":{"rendered":"http:\/\/hsp16.epgpbooks.inflibnet.ac.in\/?post_type=chapter&p=335"},"modified":"2019-08-02T06:02:37","modified_gmt":"2019-08-02T06:02:37","slug":"test-of-significance-ii-large-sample","status":"publish","type":"chapter","link":"https:\/\/ebooks.inflibnet.ac.in\/hsp16\/chapter\/test-of-significance-ii-large-sample\/","title":{"rendered":"Test of Significance II \u2013 Large Sample"},"content":{"raw":"
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1 Introduction<\/strong><\/p>\r\n \r\n

This module discusses about Test of Significance for Large Sample<\/strong>. Before moving to large samples, test of significance has to be seen in brief.<\/p>\r\n \r\n

Test of significance is performed after framing the hypothesis (tentative statements) at say 1%, 5% and 10% level. The level of significance (denoted as \u03b1 or alpha)<\/strong> represents the probability of error <\/strong>or chances of making wrong decisions. The choice of significance level stays with the researcher <\/strong>and it has to be cautiously decided based on the subject area of research. For example, for clinical researches, 1% level of significance is advisable as it needs high levels of accuracy. On the other hand, researches in social science can use significance level of 5% or even 10% as the results will not lead to fatal consequences even if it wrong.<\/p>\r\n \r\n

Given below are the significance level and its corresponding confidence level which represents the researchers\u2019 confidence level in reporting the decision. If significance level is 1%, then CI is 99% (100 - Significance level in this case) and so on.<\/p>\r\n\"\"<\/a>\r\n

The statistical significance has to be assessed using p-value. To assess statistical<\/strong> significance<\/strong>, it is important to examine the test's p-value<\/strong>.<\/p>\r\n \r\n

The following table gives the treatment for 5% level of significance and for its relevant p-value.\"\"<\/a><\/p>\r\n\r\n<\/div>\r\n

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1.2 Large Sample Test<\/strong><\/p>\r\n \r\n

Large sample means a size greater than 30. Z test is generally applied for large samples. Z<\/em>-test is a statistical <\/a>test <\/a>for approximately normally distributed data. Because of the central<\/a> limit theorem, <\/a>many test statistics are approximately normally distributed for large samples.<\/p>\r\n \r\n

Conditions to be fulfilled for Z<\/em>-tests<\/p>\r\n\r\n