## What is statistical analysis?

Statistical analysis and statistical procedure became one of the obstacles
facing the researchers in many field. These researchers know what they want to
achieve but they lack the experience which way to follow to reach that because
they lack the spiritual facts of the statistics. Many people are superior in
learning the theoretical parts of statistics but they are weak in applying that
statistics. Learning statistics theoretically make difficult to jump to its
practicality.

What makes the statistics simple is the thinking of the statistics core, which is concentrating on summarizing huge data to simple results to judge data in one aspect and to figure out the relations between the different variables included in these data.

When dealing with these two facts, one should consider the basics of statistics which is based on probability that is derived from the normal distribution of variables data.

The step by step thinking of the statistical analysis would lead any research to reach his ultimate objectives. The logical step-by-step thinking will be as follow:

Further information coming soon

What makes the statistics simple is the thinking of the statistics core, which is concentrating on summarizing huge data to simple results to judge data in one aspect and to figure out the relations between the different variables included in these data.

When dealing with these two facts, one should consider the basics of statistics which is based on probability that is derived from the normal distribution of variables data.

The step by step thinking of the statistical analysis would lead any research to reach his ultimate objectives. The logical step-by-step thinking will be as follow:

- To find out the type of distribution of the data: If the data is normally distributed this indicates that the analyzer will follow a group of statistical analysis applied to this procedure (One Way ANOVA, Two Way ANOVA, correlations), but if the data is not normally distributed this indicates that the data should be analyzed using the non-parametric analysis (Mann-Whitney test).
- Getting information about every variable included in data. The type of procedure that can be followed is reliable on the type of variable. For example, it is meaningless to run means analysis for a variable asking about sex (male and female) but it is meaningful if you get the means for a variable asking about the weight of a sample. This means the thinking about the logic of the results and its meaning for the interpretation.
- To figure out the relationships between variables, two things should be done, first, we should determine which factor affect the other and the second to search for the test that is applicable for such variables (For example: testing the effect of sex (male and female) on weight (continuous variable) one can use the two independent t-test).

Further information coming soon