SPSS

Importance of Statistical Significant

Importance of Statistical Significant

Importance of Statistical Significance

The analysis of quantitative data requires managers and decision making authorities to use statistical significance in order to generate accurate, reliable and effective result of the analysis. The importance of statistical significance can be isolated by users in effective analysis of quantitative data. The descriptive statistics assist users to convert raw data into descriptive form by using frequencies, numerical counts, central tendency measurements, variability measurement and percentages (Powell, 1996, pp.1).

The importance of statistical significance is witnessed by research as it assists users in organizing, analyzing and summarizing raw data. It can provide many benefits for the users that need to understand raw quantitative data. It is also important in communicating the findings of research along with supporting the hypothesis of the research. It also plays important role in providing reliability and credibility to research conclusions and methodology. The role of statistical testing has been important from early 1700s. It is not easy for users to develop an understanding about the raw data of quantity without effectively using the statistical significance in order to analyze the quantitative data. Therefore, the use of statistical significance becomes extremely important for organizations and researchers to develop understandable data and communicate it to further users (Mclean and Ernest, 1998, pp2-3).

The role of statistical significance is vital because it is not possible for researchers to collect information from whole population. For example: If a research is researching on the topic of consumer behaviour, it is quite impossible to receive and analyze feedback from each and every consumer. However, statistical significance enables researchers to break down population into a sample that can represent the whole set of respondents. Therefore, it makes possible for researchers to easily generate information and provide findings (Cramer and Bryman, 1990, pp.98).

Statistical Tests Example

There are various types of statistical tests that allow researchers and organizations to analyze the quantitative data. These are as follows:

One Sample t-tests

The significance to one sample t-tests is that it enables users to know significant difference between sample mean and hypothesized value. It also enables researchers to know if sample mean differs from the value of hypothesis or not. For example: The use of hsb data file helps to test if the average score of writing disagrees considerably from 50. It can be shown as:

T-test

/festival= 50

/variable = write

In this case, the mean of writing of student's sample show 52.775; it means that significant difference exists from the 50 test value. Therefore, it can be concluded that the group of students are scoring more than 50 on writing test.

One sample median test

The one sample median test enables users to test if there is any difference between sample median and hypothesized value. For example: the test of variable “write” can be assumed as an ordinal variable.

Binomial test

The binomial test enables researchers to know if the hypothesize value is different from the proportion of successes on categorical variable (dependent) on a ...