Statistical analysis is a systematic way of investigating trends, relationships, and patterns among the non-textual or digital form of data. It involves collecting, arranging, and analysing data based on a pre-defined set of principles to explore all possible patterns and trends that may either support or oppose a scientific claim. It not only helps academic researchers in writing dissertations and scholarly papers but business researchers, marketers and experts dealing with population demographics also seek help from such analysis for solving all research-related mysteries.

Knowing how different types and techniques of statistical analysis aid in interpreting data from different aspects is extremely important for completing both commercial and academic research. Hence, this article will elaborate on different types and techniques of statistical analysis in the context of writing a research paper.

**Why Writing A Research Paper Is Extremely Important?**

Research paper writing and publications permit scientific researchers to publicize novel findings for a general or targeted audience, especially those who are interested in replicating the experiments. Research papers act as research maps to guide even novice researchers to overcome all technical challenges related to a variety of fields. Writing research papers is the most sought-after medium for conveying information to scientists working in similar fields. Without writing a research paper, researchers often have to face difficulties in honing critical thinking skills; hence, lose the chance of receiving critical peer feedback. Thus, research paper writing is extremely important for those students who really want to pursue their career in the research field. Therefore, hiring the best dissertation writing services UK really matters in this regard.

**Statistical Analysis In Research Paper Writing:**

Research paper writing must start from the selection of a topic of interest. The topic of interest must state some problems related to improving the quality of existing products and services, exploring something new, and making the world a better place to live for all. Regardless of the purpose of research paper writing, steps to write an impressive research paper remain more and less the same. It starts by drafting a clear research question or hypothesis, passes by data collection, data analysis, and data interpretation, and ends by stating a clear and concise conclusion. Among all these steps, the data analysis is the most technical one that can be done by following an array of techniques. The following are some types and techniques of the statistical analysis mostly used in research paper writing:

**Type Of Statistical Analysis:**

Mainly, statistical analysis is categorised into two broad terms: descriptive statistics and inferential statistics.

**Descriptive Statistical Data Analysis:**

Descriptive statistics summarize and organise identified characteristics related to the collection of data. ^{1}In quantitative research, after data collection, the next step must be to delineate the characteristics such as average, mean, median, ratio and others. Furthermore, distribution, central tendency, and variability are three methods to conduct the descriptive statistics.

**Inferential Statistical Data Analysis:**

Inferential statistics must be the second step in the statistical analysis that aims to make inferences based on characteristics stated by using descriptive statistics. It assists researchers in either refuting or confirming a hypothesis or by guessing the generalizability of the conclusion/ set of findings for a large population. Parameter’s estimation and testing hypothesis are methods useful in drawing a conclusion using inferential statistics.

**Statistical Analysis Techniques:**

There is a wide array of statistical data analysis techniques stated in books such as Statistics by Robert S. Witte and John S. Witte, Barron’s AP Statistics by Martin Sternstein PhD, Naked Statistics: Stripping the Dread from the Data by— Charles Wheelan and more. Some of the most widely used techniques in research paper writing include mean, median, mode, standard deviations, regression, correlation, T-test, Chi-square analysis, Modification indices, Bartlett’s test, Covariance, Average variance extract, common method bias, pattern Matrix and so forth (the list may go beyond the scope of this article). Most of these techniques are subject-specific, which is why the following section will discuss only some wide-ranging techniques such as mean, standard deviation, regression, and hypothesis testing:

**Mean:**

It is the sum of all values or responses divided by a total number of entries. Mean, and average can be used interchangeably to get an overall idea or picture of the data set. Thus, the analysis that aims to take into consideration general patterns of the data set to get quick ideas must be ‘Mean’.

**Standard deviation:**

Standard deviation is a strategy that measures the spread of information around average and mean values. The high value of standard deviation refers to the wide separation of data from the calculated mean value. The standard formula to calculate the mean is as follows:

σ2=∑( x-μ)2/n

Where σ = standard deviation, Σ= sum of data, x = value of data set, μ = mean of data, and n= number of data points in the population.

**Regression:**

Regression is a statistical technique most effective for exploring the relationship between the independent and dependent variables. It helps researchers know how manipulating a variable will have an impact on another one. The main aims of regressions are to explore the type of association between variables (weak or strong) and find changes occur over time. The formula to calculate regression is as follows:

Y=a+b(x)

Where, y= independent variable, x = dependent variable, a = y- intercept, and b = slope of regression graph.

**Hypothesis testing:**

T-Testing is another more specific term we use for the process of testing a hypothesis- a statistical method of testing the two or more sets of variables within a data set. It allows researchers to validate an argument, compare data against different hypotheses, and forecast trends to make a decision while considering future outcomes. The results of hypothesis testing must be reported by calculating the p-value stating statistical significance between the selected variables.

**Final Thoughts:**

Statistical analysis is a systematic process of arranging data to observe the trends, relationships, and patterns among different variables involved in the research study. It requires use of some statistical method to make an informed or educated decision instead of following non-scientific or statistical approaches. In light of the content of this article, we may conclude that statistical data analysis for writing a research paper must include two types: differential as well as inferential statistics (one explores characteristics of a variable while the other is more concerned with extracting conclusion for making a decision). Different statistical analysis techniques such as mean, standard deviation, regression, and hypothesis testing help researchers in exploring trends and patterns in both types of statistics.