\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n
\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n\n\n\t\t\t\t\t Definition\n\t\t\t<\/path><\/svg>\n\t\t\t<\/path><\/svg>\n\t\t\t\t\t\t<\/summary>\nHypothesis testing: is a type of inferential statistics that is used to test assumptions and draw conclusions about the population from the available sample data.<\/h5>\nIt involves setting up a null hypothesis and an alternative hypothesis followed by conducting a statistical test of significance.<\/h5>\n<\/details>\n\n\n\t\t\t\t\t Hypothesis testing example\n\t\t\t<\/path><\/svg>\n\t\t\t<\/path><\/svg>\n\t\t\t\t\t\t<\/summary>\nYou want to test whether there is a relationship between gender and height.<\/h5>\nBased on your knowledge of human physiology, you formulate a hypothesis that men are, on average, taller than women.<\/h5>\nTo test this hypothesis, you restate it as:<\/h5>\nH0: Men are, on average, not taller than women.<\/h5>\nHa: Men are, on average, taller than women.<\/h5>\nTo test differences in average height between men and women, your sample should have an equal proportion of men and women, and cover a variety of socio-economic classes and any other control variables that might influence average height.<\/h5>\nYou should also consider your scope (Worldwide? For one country?) A potential data source in this case might be census data, since it includes data from a variety of regions and social classes and is available for many countries around the world.<\/h5>\nBased on the type of data you collected, you perform a one-tailed t-test to test whether men are in fact taller than women.<\/h5>\nThis test gives you:<\/h5>\nAn estimate of the difference in average height between the two groups.<\/h5>\na p-value showing how likely you are to see this difference if the null hypothesis of no difference is true.<\/h5>\nYour t-test shows an average height of 175.4 cm for men and an average height of 161.7 cm for women, with an estimate of the true difference ranging from 10.2 cm to infinity.<\/h5>\nThe p-value is 0.002.<\/h5>\nIn your analysis of the difference in average height between men and women, you find that the p-value of 0.002 is below your cutoff of 0.05, so you decide to reject your null hypothesis of no difference.<\/h5>\nIn our comparison of mean height between men and women we found an average difference of 13.7 cm and a p-value of 0.002; therefore, we can reject the null hypothesis that men are not taller than women and conclude that there is likely a difference in height between men and women.<\/h5>\n<\/details>\nHypothesis testing: is a type of inferential statistics that is used to test assumptions and draw conclusions about the population from the available sample data.<\/h5>\nIt involves setting up a null hypothesis and an alternative hypothesis followed by conducting a statistical test of significance.<\/h5>\nYou want to test whether there is a relationship between gender and height.<\/h5>\nBased on your knowledge of human physiology, you formulate a hypothesis that men are, on average, taller than women.<\/h5>\nTo test this hypothesis, you restate it as:<\/h5>\nH0: Men are, on average, not taller than women.<\/h5>\nHa: Men are, on average, taller than women.<\/h5>\nTo test differences in average height between men and women, your sample should have an equal proportion of men and women, and cover a variety of socio-economic classes and any other control variables that might influence average height.<\/h5>\nYou should also consider your scope (Worldwide? For one country?) A potential data source in this case might be census data, since it includes data from a variety of regions and social classes and is available for many countries around the world.<\/h5>\nBased on the type of data you collected, you perform a one-tailed t-test to test whether men are in fact taller than women.<\/h5>\nThis test gives you:<\/h5>\nAn estimate of the difference in average height between the two groups.<\/h5>\na p-value showing how likely you are to see this difference if the null hypothesis of no difference is true.<\/h5>\nYour t-test shows an average height of 175.4 cm for men and an average height of 161.7 cm for women, with an estimate of the true difference ranging from 10.2 cm to infinity.<\/h5>\nThe p-value is 0.002.<\/h5>\nIn your analysis of the difference in average height between men and women, you find that the p-value of 0.002 is below your cutoff of 0.05, so you decide to reject your null hypothesis of no difference.<\/h5>\nIn our comparison of mean height between men and women we found an average difference of 13.7 cm and a p-value of 0.002; therefore, we can reject the null hypothesis that men are not taller than women and conclude that there is likely a difference in height between men and women.<\/h5>\nRegression Analysis\n<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n\n\n\t\t\t\t\t Definition\n\t\t\t<\/path><\/svg>\n\t\t\t<\/path><\/svg>\n\t\t\t\t\t\t<\/summary>\nRegression analysis is used to quantify how one variable will change with respect to another variable.<\/h5>\nRegression analysis is a way to find trends in data.<\/h5>\n<\/details>\n\n\n\t\t\t\t\t Regression testing example\n\t\t\t<\/path><\/svg>\n\t\t\t<\/path><\/svg>\n\t\t\t\t\t\t<\/summary>\nFor example, you might guess that there’s a connection between how much you eat and how much you weigh; regression analysis can help you quantify that.<\/h5>\n<\/details>\nRegression analysis is used to quantify how one variable will change with respect to another variable.<\/h5>\nRegression analysis is a way to find trends in data.<\/h5>\nFor example, you might guess that there’s a connection between how much you eat and how much you weigh; regression analysis can help you quantify that.<\/h5>","protected":false},"comment_status":"open","ping_status":"closed","template":"","class_list":["post-48598","lesson","type-lesson","status-publish","hentry"],"yoast_head":"\nLesson 3: Types of Inferential Statistics - Certifeka-edu<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/certifeka-edu.com\/ar\/programs\/research-for-strategic-development-module\/lessons\/lesson-3-types-of-inferential-statistics-3\/\" \/>\n<meta property=\"og:locale\" content=\"ar_AR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lesson 3: Types of Inferential Statistics - Certifeka-edu\" \/>\n<meta property=\"og:description\" content=\"Lesson 3: Data interpretation Types of Inferential Statistics Inferential statistics can be classified into hypothesis testing and regression analysis. 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\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n\n\n\t\t\t\t\t Definition\n\t\t\t<\/path><\/svg>\n\t\t\t<\/path><\/svg>\n\t\t\t\t\t\t<\/summary>\nRegression analysis is used to quantify how one variable will change with respect to another variable.<\/h5>\nRegression analysis is a way to find trends in data.<\/h5>\n<\/details>\n\n\n\t\t\t\t\t Regression testing example\n\t\t\t<\/path><\/svg>\n\t\t\t<\/path><\/svg>\n\t\t\t\t\t\t<\/summary>\nFor example, you might guess that there’s a connection between how much you eat and how much you weigh; regression analysis can help you quantify that.<\/h5>\n<\/details>\nRegression analysis is used to quantify how one variable will change with respect to another variable.<\/h5>\nRegression analysis is a way to find trends in data.<\/h5>\nFor example, you might guess that there’s a connection between how much you eat and how much you weigh; regression analysis can help you quantify that.<\/h5>","protected":false},"comment_status":"open","ping_status":"closed","template":"","class_list":["post-48598","lesson","type-lesson","status-publish","hentry"],"yoast_head":"\nLesson 3: Types of Inferential Statistics - Certifeka-edu<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/certifeka-edu.com\/ar\/programs\/research-for-strategic-development-module\/lessons\/lesson-3-types-of-inferential-statistics-3\/\" \/>\n<meta property=\"og:locale\" content=\"ar_AR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lesson 3: Types of Inferential Statistics - Certifeka-edu\" \/>\n<meta property=\"og:description\" content=\"Lesson 3: Data interpretation Types of Inferential Statistics Inferential statistics can be classified into hypothesis testing and regression analysis. Hypothesis testing also includes the use of confidence intervals to test the parameters of a population Hypothesis testing Definition Hypothesis testing: is a type of inferential statistics that is used to test assumptions and draw conclusions […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/certifeka-edu.com\/ar\/programs\/research-for-strategic-development-module\/lessons\/lesson-3-types-of-inferential-statistics-3\/\" \/>\n<meta property=\"og:site_name\" content=\"Certifeka-edu\" \/>\n<meta property=\"og:image\" content=\"https:\/\/certifeka-edu.com\/wp-content\/uploads\/2025\/04\/logos-png-01-296x57-1.png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u0648\u0642\u062a \u0627\u0644\u0642\u0631\u0627\u0621\u0629 \u0627\u0644\u0645\u064f\u0642\u062f\u0651\u0631\" \/>\n\t<meta name=\"twitter:data1\" content=\"5 \u062f\u0642\u0627\u0626\u0642\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":[\"WebPage\",\"webpage\"],\"@id\":\"https:\\\/\\\/certifeka-edu.com\\\/programs\\\/research-for-strategic-development-module\\\/lessons\\\/lesson-3-types-of-inferential-statistics-3\\\/\",\"url\":\"https:\\\/\\\/certifeka-edu.com\\\/programs\\\/research-for-strategic-development-module\\\/lessons\\\/lesson-3-types-of-inferential-statistics-3\\\/\",\"name\":\"Lesson 3: Types of Inferential Statistics - 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