{"id":39099,"count":59,"description":"Statistical analysis<\/strong> means investigating trends, patterns, and relationships using quantitative data<\/a>. It is an important research tool used by scientists, governments, businesses, and other organizations.\r\n\r\nTo draw valid conclusions, statistical analysis requires careful planning from the very start of the research process<\/a>. You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.\r\n\r\nAfter collecting data from your sample, you can organize and summarize the data using descriptive statistics<\/a>. Then, you can use inferential statistics<\/a> to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.\r\n\r\nThis article is a practical introduction to statistical analysis for students and researchers. We\u2019ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation<\/a> between variables.\r\n
Example: Causal research question<\/figcaption>Can meditation improve exam performance in teenagers?<\/figure>\r\n
Example: Correlational research question<\/figcaption>Is there a relationship between parental income and college grade point average (GPA)?<\/figure>\r\n\r\n

Step 1: Write your hypotheses and plan your research design<\/h2>\r\nTo collect valid data for statistical analysis, you first need to specify your hypotheses<\/a> and plan out your research design.\r\n

Writing statistical hypotheses<\/h3>\r\nThe goal of research is often to investigate a relationship between variables within a population<\/a>. You start with a prediction, and use statistical analysis to test that prediction.\r\n\r\nA statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses<\/strong> that can be tested using sample data.\r\n\r\nWhile the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.\r\n
Example: Statistical hypotheses to test an effect<\/figcaption>\r\n