Search results
Apr 11, 2022 · A beautiful, free online scientific calculator with advanced features for evaluating percentages, fractions, exponential functions, logarithms, trigonometry, statistics, and more.
Statistics is the branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data, while probability is the branch of mathematics dealing with the likelihood of occurrence of different events.
- What Does A Statistical Test do?
- When to Perform A Statistical Test
- Choosing A Parametric Test: Regression, Comparison, Or Correlation
- Choosing A Nonparametric Test
- Flowchart: Choosing A Statistical Test
- Other Interesting Articles
Statistical tests work by calculating a test statistic – a number that describes how much the relationship between variables in your test differs from the null hypothesis of no relationship. It then calculates a p value (probability value). The p-value estimates how likely it is that you would see the difference described by the test statistic if t...
You can perform statistical tests on data that have been collected in a statistically valid manner – either through an experiment, or through observations made using probability sampling methods. For a statistical test to be valid, your sample size needs to be large enough to approximate the true distribution of the population being studied. To det...
Parametric tests usually have stricter requirements than nonparametric tests, and are able to make stronger inferences from the data. They can only be conducted with data that adheres to the common assumptions of statistical tests. The most common types of parametric test include regression tests, comparison tests, and correlation tests.
Non-parametric tests don’t make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.
This flowchart helps you choose among parametric tests. For nonparametric alternatives, check the table above.
If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples.
Apr 23, 2022 · Apr 23, 2022. Page ID. David Lane. Rice University. Learning Objectives. Distinguish between between-subject and within-subject designs. State the advantages of within-subject designs. Define "multi-factor design" and "factorial design" Identify the levels of a variable in an experimental design.
With some statistical experiments, each sample point is equally likely to occur. In this situation, the probability of an event is very easy to compute. It is: P (E) =. Number of sample points in event. Number of sample points in sample space. Think about the toss of a single die.
Jan 18, 2024 · The definition of descriptive analysis is the use of descriptive statistics to analyze a data set and extract valuable information about it. A descriptive analysis is sometimes called an exploratory data analysis , with the goal of understanding the data set so that we can apply statistical algorithms to it effectively.
In this lesson, we'll learn two methods, namely the method of maximum likelihood and the method of moments, for deriving formulas for "good" point estimates for population parameters. We'll also learn one way of assessing whether a point estimate is "good." We'll do that by defining what a means for an estimate to be unbiased.
