**The Introduction: **The Merriam-Webster dictionary defines the term “statistics” as “a branch of mathematics which deals with the collection, analysis, interpretation, and presentation of masses of numerical data.” The definition is very apt in what needs to be conveyed.

The field of mathematics has a very strong influence on the statistical analysis of data. It helps sort data in a way through which patterns may be observed and analysis with coherence to the hypothesis may be made. There are phases through which statistics functions and each of these phases uses a certain technique as to sort the data out in a way it becomes to recognize by the sociological scientists.

One should acquire knowledge regarding statistics as it is believed that even rudimentary knowledge of the same makes a person better at analyzing the data. This sort of information is used everywhere, be it a special task, i.e. researches or day to day life. For instance, a politician acquires statistical knowledge to know more about the issues and challenges in his region so as to acknowledge them and take the necessary steps to rectify them.

There are three basic distinct goals for sociological research; they are description, explanation, and prediction. The most important aspect of sociological research is the descriptive part as it tries to explain the reason behind the research and how it will have an effect on the current as well as the forthcoming parts of sociology. However, the general trend used by the sociologists is an attempt to explain and predict what they have observed. It’s in coherence to a personal account of the findings of the research.

**The Research Methods**

There are three basic methods of research which are most commonly used by the sociologists in their studies. They are as follows:

- Observational Techniques
- Surveys
- Experiment

The way that this data is collected and analyzed is through numeric digits. The findings of the sociologists are kept in numeric terms as to gather the data under a certain form of measurement. Every research yields a set of numbers which is then processed in order to gather findings regarding the experiment. It becomes very essential to convert this data in terms which other scientists and laymen can understand. The interpretation of this data becomes utmost necessary as it serves as proof as well as a stepping stone for new studies or research. The data needs to searched thoroughly as to see what factors affect it and what factors don’t, a set of variables need to be accounted for to make the study a success. The checking of the effect of experimental manipulations may be done too as to figure out how credible the data and experiment is.

**Representation of Data **

The most used data analysis technique in statistics is frequency distribution; it basically indicates the frequency of each score in a set of scores. The elementary function of a frequency distribution is to provide information regarding the number of occurrences of a given set of values (numeric) over a given period of time. These values are spread over the list, table or graphical representation and are classified as either grouped or ungrouped. The data is grouped using this and the relevance of each group is analyzed, hence making it easier to find patterns.

The case of dependent and independent variables is very important to understand as they tend to decide the course over which the experiment will go. A good experiment needs to have these variables as they decide what sort of relationship is being mapped. The best way to analyze the independent and dependent relationship is through a line graph. There is a variety of graphs used by sociologists to make the laymen understand the relevance and findings of the experiment, for example, histograms, pie charts, etc. Line graphs showcase the relationship between the independent and dependent variables.

**Graphical Representation**

The concept of graphical representation gives more impact to the findings of the statistical study as it helps the observer what the study wants to show and in terms which are easier for everyone to understand. There are a variety of graphs through which data is represented and each of these graphs is used for a specific representation. A graph also saves a lot of time as the observations are right in front of the observer and hence it becomes easier to find the relation between the sets of data provided. There is no need for any prior knowledge so it is understood by a larger group of people. A comparison can be done between the data in the form of graphs and the data can be understood more carefully. The graphs help define the statistical analysis of the data as they show the various aspects of the statistics, for instance, a graph showing the mean, median and mode becomes easier to understand. Also, the interpretation and extrapolation of the data become easier.

There are some rules regarding graphical representation which need to be strictly followed in order to get the desirable results for the experiment. The graph should always be titled as to understand what it is trying to convey and the measurement unit of the graph should be mentioned. There should be a proper scale to the graph along with an index. The graph should be made in the simplest manner as possible as it increases the probability of people to understand. It should be neat so no confusion may occur while analyzing it. The sources of the graph are very essential as the graph is only credible when the data it is formed from is credible.

**Descriptive Statistics **

The descriptive statistics are used to summarize a given set of data set; this can be a representation of the entire work sample or a given part of it. The basic job of descriptive statistics is to help describe and understand the features of a specific data set by giving summaries of the data set. There are two basic measures through which the descriptive statistics are measured; they are central tendency and variability. The central tendency keeps three aspects, mean, median and mode, the model describes the most recurring set of data, the median shows the middle score whereas the mean is the arithmetic average of the scores. The variability represents how dispersed a set of scores are, the variance is then calculated which is the average of the squared deviations from the mean of the scores. The standard deviation is the square root of the variance.

The data can be measured by interpreting the shape of the curve which can tell the observer as to how the data is being managed. There is basically the normal curve and the bell-shaped curve through which one can easily observe the correlation of the data.

**Correlational Statistics**

It becomes essential to observe the relationship between two or more set of scores. This is where the concept of correlation statistics comes in, it shows whether and how strongly pairs of variables are related. Some correlations are very obvious to analyze, to even the naked eye, but there are correlations which showcase many unexpected relationships between the sets of data. It also shows the degree of the correlations as to whether they are strong or weak. The more intelligent correlations are made, the more the understanding of the data set grows.

The correlations are mapped on scatter graphs as it becomes easier to observe the trends and correlations. The most effective technique used to find the correlations between data is Pearson’s product-moment correlation. The techniques help calculate the Pearson coefficient which helps show the nature of the correlation in a given set of data. There is a specific range, i.e. -1 to +1, through which the relations may be accounted for. If one gets a 0 as a Pearson coefficient then there is no relation between the data, a number above 0 indicates a positive relationship whereas a number below 0 showcases a negative relationship.

**Inferential Statistics**

It is one of the two main branches in the field of statistics. The fundamental reason to employ an inferential statistical study is to analyze whether the findings of the research may be generalized from their samples to the population that they represent. The job of inferential statistics comes in when it becomes hard to analyze and take into account the whole sample size needed for the research. For instance, in a nail manufacturing factory, if one needs to measure the diameter of each nail, it would be very hard to measure every nail. Hence, a generalized sample size of the nails and their diameter is taken, and a general observation is made regarding the same.

**The Conclusion**

It becomes very essential in sociology to carry out a statistical study as the research is very quantitative; hence it requires a correct mathematical procedure to generate results which are reliable. The statistical analysis and study provide the sociologist to share their data in terms for everyone to understand as well as form a case for further studies in the same field.

**Sources:**

- McGraw Hill. (2001). Statistics Primer for Sociology. http://www.mhhe.com/socscience/sociology/statistics/stat_intro.htm