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It introduces core concepts including null and alternative hypotheses, p-values, and how to choose significance levels using alpha and type I error. It also details assumptions required for inferential tests, methods to assess normality, and the difference between categorical and continuous data. Finally, it outlines common statistical tests used in JEI manuscripts, with guidance on applying tools such as GraphPad QuickCalcs.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/jei-stats-guide-statistical-analysis-framework/472967/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/jei-stats-guide-statistical-analysis-framework/472967.png","ImageObject",300,407,{"name":92,"@type":93},"Indoniesian Boy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-08","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of descriptive statistics in research?","Question",{"text":112,"@type":113},"Descriptive statistics summarize central tendency and variability, helping you understand the patterns in your data. 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Alpha should be set before data collection and analysis, with a typical value of 0.05 used in many cases.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},472967,1791170288,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41},962090760608,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","1. Why do we use statistics in research?  \nYou may already be familiar with “descriptive statistics”, values such as mean, median, mode, interquartile range, etc. These values can tell us the central tendency of our data (i.e., where the bulk of our data points are) or the variability of our data (i.e., how close together our data points are) . Therefore, descriptive statistics allow us to understand our data, but we cannot use it to determine how variables are interacting with each other or make conclusions about how our experimental sample may represent the larger population. To do this, we need to use “inferential statistics”.  \n2. Framework for statistical analysis  \n2.1 Hypotheses:  \nWhen you perform a statistical test, you always have two hypotheses in mind, the null (H0) and alternative (Ha) hypothesis.  \n• The null hypothesis assumes the “status quo” between your two populations-that is there is equality between them. In other words, it assumes there is no difference or change between what you are comparing.  \no Example: There is no difference in ice cream consumption between people older than  \n18 and those under 18.  \n• The alternative hypothesis assumes that there is inequality between your two populations. This is the question you actually want to test.  \no Example: There is a difference in ice cream consumption between people older than  \n18 and those under 18.  \nNote: for JEI manuscripts, you should not present your null and alternative hypotheses. Only the alternative hypothesis, in the larger context of your research, should be presented.  \n2.2  P-values:  \n2.2.1 What is a p-value?  \nIt is a value between 0 and 1 that provides a measurement of probability, assuming that the null hypothesis is true.  \n2.2.2 What does it mean?  \nThere are three main ways we can think about a p-value and its meaning. They are:  \n1. The probability we observe a test statistic as extreme or more extreme than the one observed  \n2. Probability that we observe data in our population that is at least as extreme as what we observed  \n3. If the experiment was repeated, the probability you would observe results as extreme by chance  \n2.3 How do we decide what is significant?  \nA p-value in itself does not tell us if data is significant or not. We need to set a significance level to help us decide whether our hypothesis is supported. Your significance level should always be set prior to collecting data and running any statistical analysis.  \nThe significance level is determined by “α”(alpha), which represents type I error. When you reject a null hypothesis that is true (i.e., you get a false positive result), this is a type I error. You can set your α-level to any value, but typically 0.05 is used, which means that we are okay with getting false positive results 5% of the time. Another way to think of this is saying that you are 95% confident that the results you saw are true (i.e., you have a 95% confidence interval) .  \n2.4 Assumptions to perform inferential statistics  \nWhen you perform any of the following statistical tests, your data should meet the assumptions listed below. Failure to meet these assumptions means that the results of any test you perform may not be accurate.  \n1. Your data is normally distributed (and groups are of approximately equal size)  \n2. Your sample is representative of the larger population  \n3. Observations are independent of each other  \nIf these assumptions are not met, then it means you cannot trust your p-value or confidence interval with the t-tests, or one-way ANOVA described below. In these situations, it is better to use descriptive statistics to make comparisons with your data or use a non-parametric test (described here) .  \n2.4.1 Checking for normal distribution of data  \nWe recommend checking whether your data follows a normal distribution in two ways. The first is to calculate the mean, median and mode for your data. If it is normally distributed, then these values should be equal. The second way","cbCaiaJr0gwcHDv1","https://ap.wps.com/l/cbCaiaJr0gwcHDv1","pdf",292831,12,"English","# Why Use Statistics in Research?\n## Descriptive vs. Inferential Statistics\n# Framework for Statistical Analysis\n## Hypotheses (H0 and Ha)\n## P-values\n### What is a p-value?\n### What does it mean?\n### How to decide significance\n## Assumptions for Inferential Statistics\n### Checking for normal distribution\n## Types of Data\n### Categorical data\n### Continuous data\n# Common Statistical Tests for JEI Work\n## t-Tests","[{\"question\":\"What is the purpose of descriptive statistics in research?\",\"answer\":\"Descriptive statistics summarize central tendency and variability, helping you understand the patterns in your data. They do not determine how variables interact or how results generalize to a larger population.\"},{\"question\":\"What is a p-value and what does it mean?\",\"answer\":\"A p-value is a number between 0 and 1 representing probability under the assumption that the null hypothesis is true. It can be interpreted in terms of how extreme a test statistic is relative to what would be expected under the null.\"},{\"question\":\"How do you decide whether results are statistically significant?\",\"answer\":\"Significance depends on the chosen significance level alpha (α), which relates to type I error. Alpha should be set before data collection and analysis, with a typical value of 0.05 used in many cases.\"}]","JEI Stats Guide - Statistical Analysis Framework | PDF",1790781704]