Through statistics, the collected data can be abridged and presented in such a way that it can be easily understood. When building a **statistical reporting** strategy there are three main classification of statistical tests used in surveys: descriptive statistics, inferential statistics and psychometric tests.

**Descriptive Statistics**

When using statistical reporting descriptive statistics aims to illustrate a huge portion of the collected data through charts and tables. However, it does not seek to derive a conclusion based from this information or from the sampling population. What it intends to accomplish is to give a rundown of the gathered information by using descriptive charts and tables. This type classification of statistical test is typified with uni-variate analysis and its corresponding survey sample. In addition, it is associated with measures of tendency such as the mean, median, and mode, and measures of dispersion like variance and standard deviation. Descriptive statistics are a very important variable when running statistical reports.

**Inferential Statistics**

Inferential statistics provide a more compelling and effective statistical data analysis. As the name implies, inferential statistics is involved with making broader and deeper deductions and interpretations usually on the interaction between variables, cause and effect relationship, and identifying the scope of the sample’s representation in the population. Based on the sample, the surveyor will verify the hypothesis and then come up with a conclusion. Commonly used inferential statistics in data analysis are Analysis of Variance (ANOVA), T-test, linear regression, and multiple regression.

**Psychometric Tests**

Psychometric tests analyze the attributes and performance of the employed survey to ensure that the survey data is reliable and valid. Example of a psychometric test is Cronbach’s Alpha.

## Statistical Reporting Tools

*Statistical reporting* tools are also used in further understanding the survey data, which is a key factor in making business decisions. Among these are factor analysis, cluster analysis, gap analysis, Z-test, and U-test. In a factor analysis, the obtained data are classified into recognizable clusters. On the other hand, a cluster analysis specifies data clusters that have unique And traceable attributes. Moreover, a gap analysis correlates data and determines if the data disparities are statistically important. A Z-test matches two percentile scores and determines if they are statistically important while a U-test equates median scores of axis-define groups and identifies if their differences are statistically relevant.

These statistical reporting tools can also be supplemented with tables such as frequency tables and cross tabs. Frequency tables depict all the response choices, how many times it has been answered, and the percentage of participants who chose those responses. These are beneficial especially when there are various response choices present or if there is a little disparity between the responses. When two different subgroups or subsets of data will be compared, cross tabulation or cross tab is the best way to go. It is commonly used for questions relating to demographics. Cross tab lets you correlate data from two queries so as to establish the relationship that exists between them.

### Types of Statistical Reporting Data

There are four types of data normally encountered during statistical analysis and are presented in statistical reporting data: categorical data, ordinal data, interval data, and ratio data.

**Categorical Data**– This type of data is a result of relative frequency statistics. Example is dividing the sum of a certain response with the total number of responses. Let us say that for a brand survey, the brand quality choice accumulated 25 responses out of 100. Therefore, it can be inferred that 25 percent of the participants prefer brand quality in choosing a brand.**Ordinal Data**– Ordinal data are best presented using frequency tables. These are data that have scales and ordered according to preference. For example, out of 100 respondents, 45 of them agree that the brand needs to improve its packaging. In percent, that is equivalent to 45%.**Interval Data**– Encapsulating interval data can be best be done when treated as an ordinal data. Averaging and standard deviation are the ideal techniques in evaluating this type of data.**Ratio Data**– Ratio data can be converted to a normal data using logarithms and square roots. A distinguishing characteristic of this set of data is that it has a defined zero point. Decimals and fractions are also available in a ratio data.