A hypothesis is simple if it specifies the population completely, i.e., it specifies the population distribution uniquely, while a composite hypothesis leads to two or more possibilities.<\/p>\n\n\n\n
Before diving further into their differences, let’s first define a few terms that are handy in understanding the concept of a hypothesis.<\/p>\n\n\n\n
Let’s dive in;<\/p>\n\n\n\n
Difference between hypothesis and theory.<\/h2>\n\n\n\n
A hypothesis is a proposed explanation for a phenomenon. A scientific theory is a well-substantiated explanation for an aspect of the natural world supported by a vast body of evidence. Theories <\/a>are generally much broader in scope than hypotheses and are often not as specific.<\/p>\n\n\n\n The objective of statistics is to make inferences about a population based on information contained in the sample.<\/p>\n\n\n\n There are two major areas of statistical inference, namely;<\/p>\n\n\n\n We will develop general methods for testing hypotheses and then apply them to common problems.<\/p>\n\n\n\n A statistical hypothesis is a testable statement about a population parameter. The statement is based on an assumption about the population parameter. This assumption is usually made about the population parameters based on past research or experience. The statistical hypothesis is used to make predictions about future events. These predictions are based on the assumption that the population parameters will remain the same.<\/p>\n\n\n\n A statistical hypothesis is about a population parameter, usually denoted by some symbol, such as \u03bc or \u03b8. <\/p>\n\n\n\n Statistical<\/a> hypothesis testing is a method of statistical inference. There are two types of statistical hypothesis tests: <\/p>\n\n\n\n The alternative hypothesis is the hypothesis that is being tested against the null hypothesis. The alternative hypothesis could be that \u03bc>0 or \u03bc<0. <\/p>\n\n\n\n A statistical hypothesis test determines whether or not to reject the null hypothesis. The null hypothesis is rejected if the test statistic is greater than or less than the critical value.<\/p>\n\n\n\n A hypothesis is a statement or claims about how two variables are related. Hypothesis testing<\/a> is a statistical procedure used to assess whether the null hypothesis\u2014a statement that there is no difference between two groups or no association between two variables\u2014can be rejected based on sample data. There are four steps in hypothesis testing: <\/p>\n\n\n\n The first step is to state the null and alternative hypotheses. The null hypothesis is that the two variables have no difference or association. The alternative hypothesis is the statement that there is a difference or an association between two variables. <\/p>\n\n\n\n The second step is to select a significance level. The significance level is the probability of rejecting the null hypothesis when it is true. The most common significance levels are 0.05 and 0.01. <\/p>\n\n\n\n The third step is to calculate the test statistic. The test statistic measures the difference between the null and alternative hypotheses. There are many different test statistics, and the choice of test statistic depends on the data type and hypothesis test. <\/p>\n\n\n\n The fourth and final step is to interpret the results. The results of a hypothesis test are either significant or not significant. A significant result means that the null hypothesis can be rejected. A non-significant result means that the null hypothesis cannot be rejected.<\/p>\n\n\n\n In statistics, a null hypothesis is a statement one seeks to disprove, reject or nullify. Most commonly, it is a statement that the phenomenon being studied produces no effect or makes no difference. For example, if one were testing the efficacy of a new drug, the null hypothesis would be that the drug does not affect the treated condition. <\/p>\n\n\n\n The null hypothesis is usually denoted H0, and the alternate hypothesis is denoted H1. If the null hypothesis is rejected in favor of the alternative hypothesis, it is said to be “statistically significant.” The null hypothesis is often assumed to be true until it can be proved otherwise. <\/p>\n\n\n\n Many different types of tests can be used to test a null hypothesis. The most common is the Student’s t-test, which compares the means of two groups. If the t-test is significant, there is a statistically significant difference between the two groups. <\/p>\n\n\n\n Other tests that can be used to test the null hypothesis include the chi-square, Fisher’s exact, and Wilcoxon rank-sum tests.<\/p>\n\n\n\n The alternative hypothesis is the hypothesis that is being tested in a statistical test. This is the hypothesis that is the opposite of the null hypothesis. We are trying to find evidence for the alternative hypothesis in a test.<\/p>\n\n\n\n Hypotheses can be composite or simple, and both are useful depending on the research question and the available evidence.<\/p>\n\n\n\n A simple hypothesis is a straightforward statement that proposes a relationship between two variables. It is a clear, concise statement that is easy to test and evaluate. A simple hypothesis is often used in experimental research where the researcher wants to test the effect of one variable on another.<\/p>\n\n\n\n Examples of hypothesis<\/em><\/strong>:<\/p>\n\n\n\n An example of a simple hypothesis is “students who study more will get better grades.” This hypothesis proposes a direct relationship between the amount of time a student spends studying and their academic performance. This hypothesis is testable by comparing the grades of students who study more with those who study less.<\/p>\n\n\n\n Another example of a simple hypothesis is “increased exposure to sunlight will result in higher vitamin D levels.” This hypothesis proposes a direct relationship between sunlight exposure and vitamin D levels. This hypothesis is testable by measuring the vitamin D levels of individuals with varying levels of sunlight exposure.<\/p>\n\n\n\n Simple hypotheses are advantageous because they are easy to test and evaluate. They also allow researchers to focus on a specific research question and avoid unnecessary complexity. Simple hypotheses are particularly useful in experimental research<\/a> where researchers manipulate one variable to observe its effect on another.<\/p>\n\n\n\n However, simple hypotheses also have limitations. They may oversimplify complex phenomena, and their results may not generalize to a larger population. The available evidence may also limit simple hypotheses, and additional research may be necessary to understand the relationship between variables fully.<\/p>\n\n\n\n In essence, a simple hypothesis is a straightforward statement that proposes a relationship between two variables. Simple hypotheses are useful in experimental research and allow researchers to focus on a specific research question. However, simple hypotheses also have limitations and should be evaluated in the context of the available evidence and research question.<\/p>\n\n\n\n A composite hypothesis, on the other hand, proposes multiple relationships between two or more variables. For example, a composite hypothesis might state that “there is a significant difference between the average heights of men and women, and there is also a significant difference between the average heights of people from different continents.”<\/p>\n\n\n\n Composite hypothesis testing is a statistical technique used to determine the probability of an event or phenomenon based on observed data. This technique is often used in scientific research, quality control, and decision-making processes where the outcome of a particular experiment or test is uncertain.<\/p>\n\n\n\n A composite hypothesis is an alternative hypothesis encompassing a range of possible outcomes. It is defined as a hypothesis with more than one parameter value. For example, if we are testing the hypothesis that the mean of a population is greater than a certain value, we could define the composite hypothesis as follows:<\/p>\n\n\n\n H1: \u03bc > \u03bc0, where \u03bc is the population means, and \u03bc0 is the hypothesized value of the mean.<\/p>\n\n\n\n The composite hypothesis, in this case, includes all values of \u03bc greater than \u03bc0. This means we are not specifying a specific value of \u03bc, but rather a range of possible values.<\/p>\n\n\n\n Composite hypothesis testing involves evaluating the probability of observing a particular result under the null hypothesis and then comparing it to the probability of observing the same result under the composite hypothesis. The result is considered significant if the probability of observing it under the composite hypothesis is sufficiently low.<\/p>\n\n\n\n We use statistical tests such as the t-test, F-test, or chi-square test to test a composite hypothesis. Given the null hypothesis and the observed data, these tests allow us to calculate the probability of observing a particular result.<\/p>\n\n\n\n In conclusion, composite hypothesis testing is a valuable statistical technique <\/a>used to determine the probability of an event or phenomenon based on observed data. It allows us to test hypotheses that encompass a range of possible outcomes and is an essential tool for scientific research, quality control<\/a>, and decision-making processes. <\/p>\n\n\n\n Understanding composite hypothesis testing is essential for anyone working in these fields and can help ensure that decisions are made based on solid statistical evidence.<\/p>\n","protected":false},"excerpt":{"rendered":" What is a hypothesis? A hypothesis is an educated guess about how something works. In the scientific method, a hypothesis is an idea that can be tested. If the hypothesis is correct, then the experiment will support the hypothesis. If the hypothesis is incorrect, the experiment will not support the hypothesis. A hypothesis is simple […]<\/p>\n","protected":false},"author":1,"featured_media":1685,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rank_math_lock_modified_date":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[15],"tags":[],"class_list":["post-1675","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-statistics"],"uagb_featured_image_src":{"full":["https:\/\/mbithiguide.com\/wp-content\/uploads\/2022\/10\/crissy-jarvis-cHhbULJbPwM-unsplash-scaled.jpg",2560,1700,false],"thumbnail":["https:\/\/mbithiguide.com\/wp-content\/uploads\/2022\/10\/crissy-jarvis-cHhbULJbPwM-unsplash-150x150.jpg",150,150,true],"medium":["https:\/\/mbithiguide.com\/wp-content\/uploads\/2022\/10\/crissy-jarvis-cHhbULJbPwM-unsplash-300x199.jpg",300,199,true],"medium_large":["https:\/\/mbithiguide.com\/wp-content\/uploads\/2022\/10\/crissy-jarvis-cHhbULJbPwM-unsplash-768x510.jpg",640,425,true],"large":["https:\/\/mbithiguide.com\/wp-content\/uploads\/2022\/10\/crissy-jarvis-cHhbULJbPwM-unsplash-1024x680.jpg",640,425,true],"1536x1536":["https:\/\/mbithiguide.com\/wp-content\/uploads\/2022\/10\/crissy-jarvis-cHhbULJbPwM-unsplash-1536x1020.jpg",1536,1020,true],"2048x2048":["https:\/\/mbithiguide.com\/wp-content\/uploads\/2022\/10\/crissy-jarvis-cHhbULJbPwM-unsplash-2048x1360.jpg",2048,1360,true],"blogus-slider-full":["https:\/\/mbithiguide.com\/wp-content\/uploads\/2022\/10\/crissy-jarvis-cHhbULJbPwM-unsplash-scaled.jpg",1084,720,false],"blogus-featured":["https:\/\/mbithiguide.com\/wp-content\/uploads\/2022\/10\/crissy-jarvis-cHhbULJbPwM-unsplash-scaled.jpg",1024,680,false],"blogus-medium":["https:\/\/mbithiguide.com\/wp-content\/uploads\/2022\/10\/crissy-jarvis-cHhbULJbPwM-unsplash-scaled.jpg",572,380,false]},"uagb_author_info":{"display_name":"Benard Mbithi","author_link":"https:\/\/mbithiguide.com\/author\/benard-mbithi\/"},"uagb_comment_info":0,"uagb_excerpt":"What is a hypothesis? A hypothesis is an educated guess about how something works. In the scientific method, a hypothesis is an idea that can be tested. If the hypothesis is correct, then the experiment will support the hypothesis. If the hypothesis is incorrect, the experiment will not support the hypothesis. A hypothesis is simple…","_links":{"self":[{"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/posts\/1675"}],"collection":[{"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/comments?post=1675"}],"version-history":[{"count":8,"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/posts\/1675\/revisions"}],"predecessor-version":[{"id":2246,"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/posts\/1675\/revisions\/2246"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/media\/1685"}],"wp:attachment":[{"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/media?parent=1675"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/categories?post=1675"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mbithiguide.com\/wp-json\/wp\/v2\/tags?post=1675"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
Statistical hypothesis<\/h2>\n\n\n\n
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Hypothesis Testing<\/h2>\n\n\n\n
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![\"hypothesis](\"https:\/\/images.unsplash.com\/photo-1618808338062-a8273cf5bbd1?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8N3x8dGhlb3J5fGVufDB8fDB8fA%3D%3D&auto=format&fit=crop&w=500&q=60\")
Null Hypothesis vs Alternative Hypothesis<\/h2>\n\n\n\n
Simple and Composite Hypothesis<\/h2>\n\n\n\n
Simple Hypothesis<\/h3>\n\n\n\n
Composite Hypothesis<\/h3>\n\n\n\n