{"id":10321,"date":"2024-01-05T09:35:18","date_gmt":"2024-01-05T09:35:18","guid":{"rendered":"https:\/\/support.divominer.com\/en\/?post_type=ht_kb&#038;p=10321"},"modified":"2024-01-08T03:54:01","modified_gmt":"2024-01-08T03:54:01","slug":"how-to-do-chi-square-test","status":"publish","type":"ht_kb","link":"https:\/\/support.divominer.com\/en\/knowledge-base\/how-to-do-chi-square-test\/","title":{"rendered":"How to do chi-square test?"},"content":{"rendered":"\n<p>The chi-square test is a widely used hypothesis testing method for count data, suitable for analyzing the association of <strong>categorical variables<\/strong>. Its fundamental concept lies in comparing the degree of fit or goodness of fit between the theoretical frequencies and the actual frequencies.<\/p>\n\n\n\n<p><strong><span class=\"has-inline-color has-vivid-cyan-blue-color\">Operating Method<\/span><\/strong><\/p>\n\n\n\n<p>In <strong>[Statistical Analysis] &#8211; [Basic Statistics]<\/strong>, click the &#8220;+&#8221; sign to create a new chart. Then, <strong>drag two categorical variables into the [Dimension] field and keep the [Quantity] field in the [Numerical Value] column. Check the box of &#8220;Chi-square&#8221;<\/strong> <strong>on the right <\/strong>to see the correlation between the two variables in the [Dimension] column.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" width=\"1024\" height=\"479\" src=\"https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2024\/01\/12-6-1024x479.png\" alt=\"\" class=\"wp-image-10335\" srcset=\"https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2024\/01\/12-6-1024x479.png 1024w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2024\/01\/12-6-300x140.png 300w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2024\/01\/12-6-768x359.png 768w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2024\/01\/12-6-50x23.png 50w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2024\/01\/12-6-1536x718.png 1536w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2024\/01\/12-6-920x430.png 920w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2024\/01\/12-6-600x281.png 600w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2024\/01\/12-6-320x150.png 320w, https:\/\/support.divominer.com\/en\/wp-content\/uploads\/2024\/01\/12-6.png 1593w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>The results of the chi-square test are displayed under the graph, including the chi-square value, degrees of freedom, P value, and significance.<\/p>\n\n\n\n<p>Significance markers: P \u2264 .05; <strong>P \u2264 .01<\/strong>; P \u2264 .001; and for other cases, display &#8220;not significant.&#8221; It indicates a considerable deviation between the observed and theoretical values if the p-value is very small, leading to the rejection of the null hypothesis, suggesting a significant difference between the data being compared. Otherwise, if the p-value is not small, the null hypothesis cannot be rejected, and thus, it cannot be concluded that there is a difference between the actual situation represented by the sample and the theoretical assumption.<\/p>\n","protected":false},"author":2,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0},"ht_kb_category":[5],"ht_kb_tag":[16],"_links":{"self":[{"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb\/10321"}],"collection":[{"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb"}],"about":[{"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/types\/ht_kb"}],"author":[{"embeddable":true,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/comments?post=10321"}],"version-history":[{"count":2,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb\/10321\/revisions"}],"predecessor-version":[{"id":10336,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb\/10321\/revisions\/10336"}],"wp:attachment":[{"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/media?parent=10321"}],"wp:term":[{"taxonomy":"ht_kb_category","embeddable":true,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb_category?post=10321"},{"taxonomy":"ht_kb_tag","embeddable":true,"href":"https:\/\/support.divominer.com\/en\/wp-json\/wp\/v2\/ht_kb_tag?post=10321"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}