# Data analysis and statistical methods

**Data analysis and statistical methods**

Contents

** Types of data analysis and interpretation **

- The first types of data analysis and interpretation is descriptive analysis: It is intended to describe the data briefly and does not require explanations, such as what data analysis provides for a country's census. The analysis provides no more than a summary of the questionnaire's gender, age, address and so on.
- The second types of data analysis and interpretation is exploratory analysis: Exploratory data analysis attempts to find relationships, discoveries, correlations, tendencies of measurements for several variables in order to find certain ideas and hypotheses. An example of exploratory analysis is the work of a group of amateurs who analyzed many space data collected by the Kepler telescope and found a four-planet solar system by analyzing the properties of light.
- The third types of data analysis and interpretation is deductive analysis: One of the most common data analyzes in scientific research, goes beyond exploratory analysis to see if the patterns discovered are valid to be behind the totals of data available. An example is to reveal the relationship between environmental pollution and life expectancy at the state level in the United States. This analysis measures and calculates the different relationships between the available measurements.
- The fourth types of data analysis and interpretation is predictive analysis: While the previous type measures relationships and calculates their values, predictive analysis predicts certain measurements from existing ones. For example, statistical institutions do predict the outcome of an election by analyzing the prediction behavior observed in the questionnaires.
- The fifth types of data analysis and interpretation is causal analysis: This analysis calculates certain measures if other measures change as calculating the impact of a particular medical practice on reducing the incidence of a particular disease.
- The sixth types of data analysis and interpretation is mechanical analysis: The previous causal analysis finds a relationship that has a certain rate of occurrence and the impact of data may be very large, for example, for decades data says that smoking leads to cancer, but it is not certain you may not die cancer despite smoking. What mechanical analysis does is to establish a definite and inevitable relationship between two measurements.

Each of the above types of data analysis and interpretation has many **examples**. The example of causal analysis is the process of calculating the impact of a particular medical practice on reducing the incidence of a particular disease. In addition, the example of exploratory analysis is the work of a group of amateurs who analyzed many space data collected by the Kepler telescope and found a four-planet solar system by analyzing the properties of light.

** Disadvantages of traditional statistical methods in analysis of the questionnaire **

- Traditional statistical methods in analysis of the questionnaire takes a lot of time because there are thousands of vocabularies that researchers are working to refute, so it becomes very difficult to reach results.
- One disadvantage of traditional statistical methods in analysis of the questionnaire is that the larger the sample size, the greater the error in the result, especially as it depends on the mental effort of the scientific researcher. The results may therefore be inaccurate, affecting the final conclusions, which would conceivably answer the hypotheses.
- Another disadvantage of traditional statistical methods in analysis of the questionnaire is the existence of many quantitative measures of statistical systems such as repetitions, deviation, regression and confidence. These things cannot be done by scientific researchers at the faculty and require considerable experience in dealing with them manually.

** Statistical analysis in research **

It is highly known that the computer is very important for researchers in the process of statistical analysis in research. The following points are illustrating the importance of using computers in statistical analysis in research:

- The computer plays a major role in facilitating the task of researchers who use statistical analysis programs, which has led to a great speed in extracting data and conducting statistical analysis for it.
- The computer also plays a major role by allowing the researcher to use more than one variable at the same time.
- The computer is also characterized by the speed of showing results, in addition to the results that appear are accurate, and there is no error in them.
- The computer saves researchers time and effort, because the researcher avoids performing manual calculations that can make mistakes during the procedure.
- The computer also facilitates the researcher's task to link a number of computers to each other, and send the results to these computers in an instant, which leads to the supervisors being informed of this message immediately and making all observations in order for the student to amend it directly.
- The computer also allows the researcher to know the time it took for him to study in order to answer the questions that the researcher asked, and this is done through setting a set of instructions through which the researcher can know these things.

Thus, we see that conducting statistical analysis requires that the researcher follow a set of steps, in order to be able to reach correct results very quickly, and the computer has contributed to developing statistical analysis and making it faster.

** Why do scientific researchers use online statistical analysis? **

There is no doubt that some scientific researchers do not have extensive experience in how to analyze the statistical data of their research. Many researchers use online statistical analysis to ensure accurate scientific results that meet their scientific research objectives.

Moreover, scientific researchers use online statistical analysis in order to obtain assistance in analyzing the data they obtained from the sample on which scientific research was conducted. It is also worth noting that scientific researchers use online statistical analysis due to the limited time they are exposed to while conducting their research.

**With greetings: Al - ****Manara Consulting to help researchers and graduate students **