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Study with Quizlet and memorize flashcards containing terms like z-score, Single sample t test, independent samples t test and more.
Study with Quizlet and memorize flashcards containing terms like descriptive statistics, correlational statistics, inferential statistics and more.
- Overview
- Statistical Tests
- What does it do?
- When to perform?
- Choosing right one
This article provides information on statistical tests, including when to perform a test, how to choose the right one for your data and what assumptions are made by statistical tests. It also explains the difference between quantitative and categorical variables, as well as discrete and continuous variables.
Statistical tests are used in hypothesis testing to determine whether a predictor variable has a statistically significant relationship with an outcome variable or estimate the difference between two or more groups. They assume a null hypothesis of no relationship and calculate p-value to see if observed data falls outside of predicted range.
A statistical test works by calculating a test statistic that describes how much the relationship differs from the null hypothesis, then calculates p-value which estimates likelihood of observing this difference if there is no real relationship.
You can perform statistical tests on data collected through experiment or probability sampling methods as long as sample size is large enough and meets certain assumptions such as independence, homogeneity, normality etc.
To choose the right one you need to know your data's assumptions and types of variables (quantitative/categorical) being dealt with. Parametric tests have stronger inferences but stricter requirements while nonparametric ones make weaker inferences but don't make many assumptions about data distribution.
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Nov 4, 2018 · In this post, you’ll learn about the differences between one-tailed and two-tailed hypothesis tests and their advantages and disadvantages. I include examples of both types of statistical tests. In my next post, I cover the decision between one and two-tailed tests in more detail.
Jan 31, 2020 · A t test is a statistical test that is used to compare the means of two groups. It is often used in hypothesis testing to determine whether a process or treatment actually has an effect on the population of interest, or whether two groups are different from one another. t test example.
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Statistical tests are essential tools used to analyze data and make conclusions based on the data’s significance. In this article, we will dive into the basics of statistical tests, their significance, and their different types.
