What statistics are used to test data like this?

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Thread starter Another
Start date Apr 20, 2020
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Data Statistics Test

In summary, the purpose of using statistics to test data is to analyze and interpret information in an objective and systematic manner, leading to informed decision-making and conclusions. The most commonly used statistical tests for data analysis include t-tests, ANOVA, chi-square test, regression analysis, and correlation analysis, with the selection depending on the type of data and research question. The choice of statistical test is determined by factors such as data type, number of groups, and research question, and it is important to consider the characteristics of the data before selecting a test. There are two types of statistical tests: parametric, which assume a normal distribution, and non-parametric, which do not. Parametric tests are more powerful but require data to meet certain

Apr 20, 2020

Another

TL;DR Summary: I have 100 data. if I want to use data from 10 to 100 or from 20 to 100, which statistic should I use to test whether I can use data from 1 to 100 or 20 to 100 without significance?

I have 100 data. if I want to use data from 10 to 100 or from 20 to 100, which statistic should I use to test whether I can use data from 1 to 100 or 20 to 100 without significance?

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Apr 20, 2020

Dale

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Your question is not very clear. It sounds like you are doing 10-fold or 5-fold cross validation. What significance are you looking for? I.e. what difference are you concerned about being significant?

Related to What statistics are used to test data like this?

1. What is the purpose of statistical testing?

Statistical testing is used to determine whether there is a significant difference between groups or if a pattern observed in the data is due to chance. It helps researchers make decisions and draw conclusions based on data.

2. What are the most commonly used statistical tests?

The most commonly used statistical tests include t-tests, ANOVA, correlation analysis, and regression analysis. These tests can be used to compare means, assess relationships between variables, and make predictions.

3. How do you choose the appropriate statistical test for your data?

The choice of statistical test depends on the type of data being analyzed, the research question, and the assumptions of the data. It is important to carefully consider these factors and consult with a statistician if necessary to determine the most appropriate test.

4. What is the difference between parametric and non-parametric tests?

Parametric tests assume that the data follows a specific distribution, such as a normal distribution, and use numerical data. Non-parametric tests do not make these assumptions and can be used for non-numerical data. Parametric tests are more powerful, but non-parametric tests are more robust to violations of assumptions.

5. How do you interpret the results of a statistical test?

The results of a statistical test are typically presented as a p-value, which indicates the probability of obtaining the observed results by chance. A p-value less than 0.05 is considered statistically significant, meaning that the results are unlikely to be due to chance. It is also important to consider the effect size and confidence intervals when interpreting the results of a statistical test.