There are various statistical tests that depend on the nature of the studies. With the help of statistical tests, one can make relevant quantitative decisions of a specific sample. **Statistics test** is the testing of the hypothesis, which is used to signify the observation of the samples. Statistical solutions can be assisted with the help of selections and analysis of the proper statistical test for the dissertations. There are various key concepts of statistical tests which can help to understand the statistical tests.

**Type I error: **This is a type of error that occurs in the **statistics test** that is committed when the right sample is rejected.

**Type II error: **This is another type of error in statistical tests that is committed if a wrong sample is accepted.

Therefore, we can conclude that **statistics tests** can be categorized into several kinds that depend on the type of field. Statistical tests are used in the fields of medicine, business, psychology, and nursing.

**What is the purpose of using statistics tests?**

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**Statistics tests** are used by measuring the number of statistical data that describes the relationship between the tested variables, which differ by the null hypothesis of non-relational variables. Further, one needs to calculate the p-value (probability value), which is used to estimate how the null hypothesis of non-relationship has true value when the described difference of the test statistics changes with the value of the null hypothesis.

If the statistical test’s value is greater than that of the calculated value of the null hypothesis, then one can consider the significant relationship between the output and that of predicted variables.

But if the statistic test’s value is not greater than the measured value of the null hypothesis, then one can take it as no significant relationship between the output and that of predicted variables.

**When to use the statistics test?**

If the collected data is described in a valid statistical manner, then one can use a **statistics test** on that particular data. The collected data can be either observational data or experimental data, which is made through the method of probability sampling. The valid statistical test depends on the sample size that is large enough to estimate the true distribution value of the studied population. To know which **statistics test** is required, one requires to know these two things:

- The type of variable which you are using in your calculation.
- Whether the data meets some of the assumptions or not.

**Types of variables**

The types of variables one is using determines which type of **statistics test** you need to use. **Quantitative variables**** **are used to show the number of things, such as to calculate the number of trees in a specific forest. There are different kinds of quantitative variables that involve: **continuous and discrete variables. ****Categorical variables **are used to represent the group or the number of things, like the kinds of tree species in a particular forest. There are three kinds of categorical variables that involve: **ordinal, nominal, and binary data.**

Select the test which fits with the kind of output and predictor variables that one has collected and if one is performing an experiment, and then they can be independent and dependent variables.

**Statistics assumptions**

Statistical tests can be made through the common assumptions of the particular data which one is testing. There are three different types of statistical assumptions that are: **independence of observation **which includes the tested value with no relation, **variance’s homogeneity **that has the variance that is compared with the similar groups, and **normality of data **that follows the pattern of a normal distribution which applied on the quantitative data.

If any of the data is not matched with the homogeneity of variance or normality assumptions, then one can perform a non-parametric **statistics test**, this grants to do comparisons of the data distribution without any assumptions. But if the data is not matched with the independence of observations, then one can utilize the data for structuring it.

**Select a parametric test**

Parametric **statistics test** is used to test the data that can make strong inferences, and these are conducted with the data which adhere to the similar assumptions of the tests. There are three common types of parametric tests that involve: **regression, comparison, and correlation tests.**

**Regression tests**

It is used to test the “cause and effect” relationships. It tests the effect of single or multiple continuous variables on other variables.

Parameters | Predictor Variable | Output Variable |

Simple linear regression | 1 Predictor Continuous | 1 OutputContinuous |

Multiple linear regression | 2 or more predictorContinuous | 1 OutputContinuous |

**Comparison tests**

It is used to check the difference of group means, and one can use this test to check the effects of a categorical variable for the mean value of certain characteristics. The T-test is used to compare the means of two groups, whereas, ANOVA test is used to compare the means of multiple groups.

Parameters | Predictor Variable | Output Variable |

Independent t-test | 1 PredictorCategorical | The group becomes from dissimilar populationQuantitative |

Paired t-test | 1 PredictorCategorical | The group becomes from a similar populationQuantitative |

ANOVA | 1 or many predictorsCategorical | 1 OutputQuantitative |

**Correlation tests**

It is used to test the two variables which relate even without the value of “cause and effect” relationships. This test is used to check the variable that one wants to use for the several regression tests that are autocorrelated with each other.

Parameters | Predictor Variable | Output Variable |

Chi-Square | Categorical | Categorical |

Pearson | Continuous | Continuous |

**Select a non-parametric test**

This test does not have any assumptions of the data, and it is used when single, or multiple statistics assumptions can be split. The inferences of this test are not as strong as that of parametric tests.

Parameters | Predictor Variable | Output Variable |

Wilcoxon Signed-rank test | 2 Groups Categorical | The group becomes from a similar populationQuantitative |

Wilcoxon Rank-Sum test | 2 Groups Categorical | The group becomes from a dissimilar populationQuantitative |

Sign test | Categorical | Quantitative |

**Conclusion**

This blog has provided all the relevant information about the **statistics test** and what is the purpose of using these tests, when one can use them, and much more. All these tests depend on the types of variables and statistics assumptions, and then these tests can be done on the basis of parametric and non-parametric. This blog can help you to understand the values of statistical tests so that one can use it for a different purpose.

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