Non-symmetric statistics are one of those statistical branches that don't assume certain distribution for analysed data. This method differs from parametric statistics, which often assumes that data comes from normal distribution or any other known distribution. Non-symmetric statistics are used when data does not meet the assumption of parametric distribution, for example in situations where small, ordinary, undistributed data is normal. One of the most common nonparatric statistical methods is test. This article will discuss what nonparatric statistics are, how the chip-squared test works, as well as the application in various areas.

What's Nonparametric Statistics?

Non-parametric statistics are a method of data analysis that doesn't depend on the distribution parameters of the population. It means that this method does not require assumptions about population distribution, like normal distribution, which is one of its perks. Some of the major advantages of nonparametric statistics are:

1. Flexibility: Can be used on data that doesn't meet normal distribution assumptions.
2. Coordinate properties: Can be used to analyze ordinary data, such as the Likert scale, or data that doesn't have a clear measurement scale.
3. Residence against outlier: Not so sensitive to outlier or extreme data existence.

Non-symmetric statistics are generally used in ordinal, nominal data analysis, or undistributed data, and often applied to social, psychological, economic, or field areas where real-world data is not always in accordance with previously assumed distribution.

Exclusive Test Chi-Quadrat (Chi-Squared Test)

The chi-squared test is one of the most common nonparatric tests used in statistical analysis. This test was used to test the relationship or difference between two or more category variables. There are two major kinds of ba-squared tests:

1. Quality Test (Goodness of Fit Test): Used to determine whether observation distribution matches expected theoretical distribution.
2. Independent Test (Test of Independence): Used to test whether two variable independent categories or not.

In general, a test of chi- squared is done by comparing the observed frequency to the expected frequency. The basic formula for the chi- squared test is:

Here:

• That's the frequency that's observed in the category.
• Eee@@
• 2chi ^ 2x2 is a child-squared value generated.

The value of the chi-squared produced then compared to the distribution of the chi to determine whether there is a significant difference between the observed and expected frequency.

Step-Step Test Chi-Quadrat

1. Commencing Hypothetically Zero (H spawning) and Alternative Hypothetically (H):
   • There's no significant difference between observed and expected frequencies.
   • There's a significant difference between observed and expected frequencies.
2. Calculates the Frequency of the Dead and the Expected: Data observations of category-variable variables collected, then calculated frequencies expected based on assumption of a zero hypothesis.
3. Calculating Quadrant Chi-Quadrant Value: Use the x-squared formula to calculate the difference between observed and expected frequencies.
4. Compare to Critical Value: The value of a girl-square that counts compared to the critical value of the Chisquared table, which depends on a degree of freedom (degree of freedom) and the level of significance, usually 0.05).
5. Decisions: If the value of the girl-squared is greater than the critical value, then the zero hypothesis is denied, which means there is a significant difference between observed frequency and expected.

Chi-Quadrate Test Application

1. Social Research and Humanity: Test chi- squared is often used in social research to evaluate the relationship between category variables. For example, this test can be used to determine whether there is a relationship between gender and political preference in a population.
2. Program Effectiveness Testing: In program effectiveness testing, for example in educational research, the chi- squared test can be used to compare the results (for example, student graduation percentage) with expected results based on the program.
3. Marketing(Laughter) For example, companies can test whether the preference to two different brand products is significantly different between different age groups.
4. Biology and HealthIn medical science, test chips are often used to determine the relationship between risk factors and diseases. For example, epidemiological studies might use these tests to evaluate the relationship between smoking habits and specific diseases.

Conclusion

Non-symmetric statistics, particularly the chi- squared test, are a very useful tool in data analysis when assuming parametric distribution assumptions are not met. The chip-square test allows researchers to test the differences or links between the variable categories without relying on specific distribution. The application is extensive, including social, economic, health and marketing and other disciplines that require data categories.

sumbe: Siegel, S., & Castellan, N. J. (1988). Nonparametric Statics for the Behavioral Sciences. McGraw-Hill.