Statistical tests are use to determine whether two sets of data are statistically different from one another. In order to accomplish this, statistical tests use several statistical measures, including the mean, standard deviation, and coefficient of variation. Once the statistical measures have been calculate, the statistical test will compare them to a set of specified criteria. The statistical test will conclude that there is a significant difference between the two sets of data if the data meet the conditions (Arbia, 2021).
Depending on the type of data being study, several statistical tests can be applied. T-tests, chi-squared tests, and ANOVA testing are three of the most used statistical tests.
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Types of Statistical Tests
While working with statistical data, a variety of tools can be use to analyze the data.
1- Parametric Statistical Tests
In comparison to non-parametric tests, parametric statistical tests have stricter constraints. They also draw a strong conclusion from the data. Additionally, they can only be perform using data that adheres to statistical test assumptions. Regression testing, comparison tests, and correlation tests are examples of common parametric tests.
1.1. Regression Test
Regression tests are use to identify cause-and-effect relationships. They can be use to calculate the effect of one or more continuous variables on another.
- Simple linear regression is a sort of test that uses a straight line to describe the relationship between a dependent and an independent variable. The link between two quantitative variables is determine by this test.
- Multiple linear regression uses a straight line to assess the relationship between a quantitative dependent variable and two or more independent variables.
- Logistic regression is predicted and classifies the research problem. Logistic regression helps in the detection of data anomalies that may be suggestive of predicted fraud.
1.2. Comparison Tests
The differences between the group means are determined through comparison tests. They can be used to examine the impact of a categorical variable on the mean value of other categories.
The t-test, which is used to compare the means of two groups, is one of the most common statistical tests (e.g. the average heights of men and women). When you don’t know the population parameters, you can use the t-test (mean and standard deviation).
It analyzes the relationship between two variables related to the same population (pre-and post-test scores). For instance, comparing the trainee’s performance score before and after the training course is over.
The independent t-test is also called the two-sample t-test. It is a statistical test that is used to whether there is a statistically significant difference between the means in two unrelated groups. For example, comparing cancer patients and pregnant women in a population.
One Sample T-test
In this test, the given mean is contrasted with the mean of a single group. For example, calculating the rise and fall in sales relative to the specified average sales.
ANOVA (Analysis of Variance) examines how the means of multiple groups differ from one another. Two-way analyses contrast samples with distinct variables, whereas one-way ANOVAs analyze how one factor affects another. By comparing the means of various samples, it shows the impact of one or more factors.
Regression analysis and variance analysis for multiple dependent variables by one or more component variables or covariates are provided by MANOVA, which stands for Multivariate Analysis of Variance. It also compares the statistical characteristics of an independent grouping variable and a single continuous dependent variable.
It is a statistical test that detects if two population means differ, assuming that the variances are known and the sample size is large.
1.3. Correlation Tests
Correlation tests check if the variables are related without hypothesizing a cause-and-effect relationship. These tests can be used to identify if the two variables you want to use in a multiple regression test are correlated.
- Pearson Correlation Coefficient
It is a general way of measuring the linear correlation. The coefficient is a number between -1 and 1 and determines the strength and direction of the relationship between two variables. The change in one variable changes the course of another variable change in the same direction.
2- Non-parametric Statistical Tests
Non-parametric tests make fewer assumptions about the data than parametric tests. They come in useful when one or more common statistical assumptions are violated. These inferences, however, are not as precise as parametric tests.
The chi-square test is used to compare two categorical variables. Additionally, calculating the chi-square statistic value and comparing it to a critical value from the chi-square distribution helps you to determine whether the observed frequency differs considerably from the expected frequency.
7 Essential Ways to Choose the Right Statistical Test
1. Research Question
The choice of a statistical test is determined by the research question to be answered. Furthermore, the research questions will assist you in developing the data structure and research strategy.
2. Formulation of Null Hypothesis
You could create a null hypothesis after you’ve defined the research question. A null hypothesis implies that there is no statistical significance in the predicted observations.
3. Level of Significance in Study Protocol
A level of significance is set before starting the study protocol. The degree of significance determines the statistical significance, which decides whether the null hypothesis is accepted or rejected.
4. The Decision Between One-tailed and Two-tailed
You must pick whether your study will be one-tailed or two-tailed. You must use one-tailed tests if you have clear proof that the data go in one direction. Therefore, if there is no clear direction of the expected difference, a two-tailed test is required.
5. The Number of Variables to Be Analyzed
Statistical tests and procedures are classified based on the number of variables they are designed to evaluate. As a result, when selecting a test, you must consider how many variables you want to evaluate.
6. Type of Data
It is important to define if your data is continuous, categorical, or binary. In the case of continuous data, you must also evaluate if the data is normally distributed or skewed in order to determine which statistical test to use.
7. Paired and Unpaired Study Designs
Because the two samples are dependent on each other, a paired design includes comparison studies in which the two-population means are compared. The results of the two samples are grouped and compared in an unpaired or independent research design.
You’re on your way to discovering the correct statistical test for your research question now that you’ve learned the seven steps for selecting a statistical test. Because every situation is different, it is important to comprehend all of your options and make an informed decision.
Remember to always consult with your principal investigator or statistician, or software, if you are unsure which test to choose. Even you can also Pay Someone to Do My Online Statistics Class service if you are trying to play in numbers.
Arbia, G., 2021. Statistics and Empirical Knowledge. In Statistics, New Empiricism and Society in the Era of Big Data (pp. 21-44). Springer, Cham.
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