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Over 1200 PhD researchers have expressed that the Mann-Whitney U Test is "one of the most important tests" they have ever used. Many of these researchers have even compared the effectiveness of the Mann-Whitney U Test to that of the TAT test used in psychology because of its importance in the PhD research. But what is the reason behind it? In this blog, we will be talking about what Mann-Whitney U Test is and why you should choose it. Not only this, there are some other hidden truths to know about this test. So, let’s begin.  

The Mann-Whitney U test, also known as the Mann-Whitney-Wilcoxon test or the Wilcoxon rank-sum test, is a non-parametric statistical test used to compare two independent samples. It helps identify whether two sets of observations are from the same population or whether one set of observations typically has higher or lower values than the other.

Stylistic proofreading is used to check the style or the tone of a written text in order to improve its style i.e., improving its readability. But how to improve the readability?

By improving the grammar, spelling and punctuation of a text. Not only that, stylistic proofreading is also used to verify the appropriateness of the language for better readability.

Have you used Grammarly? If you haven’t, you can give it a try but if you have used its premium version, then you can see that it gives suggestions to change the whole structure of some sentences for better readability, But why am I saying this?

It’s because stylistic proofreading also does the same thing, But then what is the difference between stylistic proofreading and Grammarly? 

The difference is that stylistic proofreading does not only suggest the sentence structure, it also further suggests better word choice and phrasing that includes:


Reorganizing paragraphs or sentences, removing redundant or unnecessary words. In this blog, we will be talking about ‘why’ to choose and ‘how’ to use stylistic proofreading. The ‘why’ has been discussed previously but not completely.

Let us start with the ‘why’ to choose stylistic proofreading, the ‘why’ which you are yet to unravel.

When you have improved the grammar and coherence of the text, then it won’t only improve the readability but also it will be compelling and engaging for the readers. Now you may be thinking, just this? 

No, my friend. Check out the next paragraph.

It significantly improves the credibility of the documents by making them free from inconsistencies and errors. Just think about the importance of credibility when you are thinking about publishing your research paper in SpringerLink and ResearchGate. 

Now, let us know how.


7 steps you have to follow to use stylistic proofreading in your research which is described below:

  • Carefully review the text: When you will review the entire text, you will get an idea about the overall tone and style of the write-up.

  • Identify your purpose and the audience: If you understand what impact you want to create with your research and the people who you want to help. Then, you can choose the necessary style or tone according to that.

  • Now what? check for consistency: When you have completed the above steps, now it’s time to check for grammar, spelling and punctuation. Remember, the whole write-up should be consistent.

  • Let us improve the coherence and flow: I hope you know the answer to how to improve the coherence and flow. Kindly comment on what is the answer. If you don’t know, please read the first page of the blog.

  • Now it’s time to check the appropriateness of the language:  This is also discussed on the first page of this blog. The reason I am stating the same things again is to understand which step to perform first and which one to perform second.

  • Try to understand the impact: Though it is a research project, the write-up should be compelling and persuasive. The reason is that when the readers will be involved in the writing, then won’t they remember every word? If you have any other thoughts on this, kindly comment your thoughts.

  • Finally, review for inconsistencies and errors: Now, finally review everything from the top to the bottom that you have done. If everything is good, then we can think of publishing it.

Why have I said to think again before publishing your research? Though we have made all the changes, it’s always better to reach out to our mentor before publishing our research paper. They know many things which we do not know.

Now, what is the other information we can get on Stylistic proofreading? We have known all the bright sides of it, but what about the DARK side? 


That’s why we will know the problems associated with stylistic proofreading. So, here we go.   



There are 5 problems which you should be aware of while using stylistic proofreading which is described below:

  • It is subjective: Look, I can think Albert Einstein is one of the best scientists of all time, you may also think that but someone else may not think that which means it is subjective in nature. Just like that, someone may not like the style of stylistic proofreading but someone may. So, this is something to be aware of.

  • With editing, the problem of OVER-EDITING comes: But what is over-editing? Suppose the proofreader suggested so many changes, now after making so many changes, you cannot recognize your work 🤔. I mean now it is the words of the proofreader, not yours. So, it won’t be called your work, it will be the proofreaders’ work.

  • The meaning of your research can change: Let me explain. When you are making a change suggested by the proofreader, the meaning of the text can also change if the proofreader doesn’t have any idea about the research. So, you also need to be aware of this.

  • Now, the cost comes: With the increase in cost, the INCREASE IN TIME also comes. If the research paper is long and complex, not only it can be costly and time-consuming but also it can seem like impractical work.

  • Finally, limitations in STYLE: If you want to incorporate creative or poetic writing in your research, then maybe stylistic proofreading is not for you. I told you maybe because then you have to choose a proofreader who can take a flexible approach to maintain your voice and style.  

If you want us to help you to deal with the problems associated with stylistic proofreading, then kindly comment below so that we can help you with this.
I hope that you have learned all about stylistic proofreading from this blog. If you have any questions regarding this, kindly comment below so that we can answer this.

Also, you can comment if you want to know the tricks and tips to effectively proofread your dissertation and the benefits of using a Professional academic proofreading service


You can also visit our website to learn more about us.


Thank you for your time.

Data, also known as the backbone of research, is a base, on which the entire research depends on. After months of backbreaking research, scholars gather huge amounts of data (relevant as well as irrelevant). The collected data must be integrated and organised in an apt manner. And this can be done through the data analysis process. Data analysis, the process of evaluating data using statistical and analytical tools to obtain meaningful information is an ordeal in every scholar life. 

It an integral part of a research and breaks down the complex problem into simple ones, provides a theoretical base to the study and lends credibility to researched data. Today, there are several tool/software available out there which lets you conduct data analysis. This includes SPSS, Excel, Tableau public, and many more. Among all these, the most popular data analysis tool is R language which is widely used in research and academics.


R, developed in the 90s, is a robust language that is used to evaluate and organise data in a research. R offers several statistical techniques such as linear & non-linear modelling, time series analysis, clustering, etc. One of the strengths of R is that it lets you develop quality plots and contains plenty of mathematical symbols and formulae required to conduct the test. 


However, R requires an integrated software for data calculation, manipulation, and graphical display. The environment includes


  • Data handling and storage facility
  •  Operators for calculations on arrays
  •  Coherent and integrated collection of data analysis intermediate tools 
  •  A simple and well-developed programming language consisting of conditional loop and input/output facilities.

Of the several benefits of R programming language for data analysis, a few are:

  1. Missing values - Real data consists of missing values. However, missing values are a significant part of the R language.  R includes several functions that have argument and knows how to handle the missing values.
  2. Interactive language - Data analysis is an interactive process. What you see/done at one stage determines the next and hence, interactivity is important; which is present in R.
  3. Functions as a first class object - Functions such as mean and median, are objects that can be used like a data. R lets you change your analysis to utilise the median function rather than the mean, at ease.
  4. Graphics - Graphics are the crucial feature of the data analysis. This is because, it is effortless to explore data by developing the relevant graphs. R has several graphic function which lets you convey the important features of data. 
  5. Data wrangling - R has several packages that simplifies the task of preparing the data for data analysis. The data cleaning as well as transformation is also a straightforward process in R thereby helping you to reduce the time spent on them.  

R has numerous features that can assist you to perform your data analysis at ease and accurately. Hence, spend some time on learning this language in the initial stage and then clean, manipulate and conduct data analysis efficiently.

To make a valid decision about using an intercession, you cannot rely on the outcomes obtained from a single study. This is so because the results may vary from one study to another for different reasons such as confounding factors and use of distinct study samples. That is when the meta-analysis comes into view. 

Meta-analysis is basically a statistical analysis of multiple studies within your area of study. The principle of meta-analysis is the identification of common concepts in all conceptually similar studies with a specific degree of error within each study. Widely used in the area of medicines, meta-analysis process synthesises data athwart the studies, quantifies & analyses inconsistencies across the studies, and investigates publication bias. 


Today, many journals embolden research scholars to submit meta-analysis paper that summarises the body of evidence of a certain research question. For example, a research document that reports outcomes of a primary study may consist of meta-analysis in the introduction section to synthesise prior data and place a new study within the context. However, undoubtedly penning-down such kind of paper is strenuous task. Essentially meta-analysis includes section such as 

  1. Impressive topic - The secret of crafting a perfect meta-analysis paper is identifying a topic which includes the body of the text. To find a topic, you may explore the previous findings that provides explanation to the known concepts.
  2. Abstract - Although conciseness needs to be maintained in this section, you cannot afford to miss significant concepts. The abstract must include subsections such as background, selection criteria, data sources & synthesis, and quality criteria. 
  3. Introduction - This section must provide an in-depth background to the current review including the reviews of the previous studies and state of the art knowledge in the area of study. Also the significance of the review must be included here. 
  4. Methods - This is the most important section in the meta-analysis paper. This section must provide information about the search strategies (including data-base). Not just this, include types of studies & participants, approaches utilised in assessing the methodological quality of the study, how the heterogeneity of the studies were evaluated and in-depth information about subgroup analyses. In this chapter, you must explicitly define the significant outcome measure(s). Approaches used for data collection and analysis must be explained to enable an independent reader interpret the results. Also the information regarding the handling of missing data and existence of biases must be mentioned.
  5. Results - This section must include the total number of studies that were analysed and also the aggregate number of participants. Summary features of the studies must be included such as sample sizes, design, interventions, and outcomes. An analysis of the relative distribution of potential variables among the various studies should also be enlisted.
  6. Conclusion & discussion - The conclusion chapter must be purely based on the data and not on your personal point of view and must restate the crucial findings. The discussion must enlist the factors that assist to interpret the crucial findings as well as possible causes of bias. The discussion should also enclose the quality of the evidence, completeness of the evidence, and the possibility of bias. 


Now that you know how to craft a meta-analysis paper, fold your sleeves and write a paper which is apt and structured. Remember, if crafted in a flawless manner, it can significantly contribute to the existing knowledge of your study area. 

You have got a title of Dr, and now wondering whether to stay in academia or not whilst your supervisor constantly bugs at you, and you have become bored of working in labs, you are not alone. You might think that you are irreplaceable as no one can know about your research better than you, but unfortunately you are living in illusion. There’s a huge supply of academic PhD scholars. No matter how much your supervisors brag about the benefits of staying in academia, it’s not going to be useful for you. Here are the reasons why you should leave academia after qualifying your PhD.

Huge Supply of PhD Scholars
According to a 2014 survey, the world is constantly producing PhDs. It has produced more PhDs than ever before. The survey found that 67,449 people qualified as PhDs in the US. The next country is the United Kingdom that has pipped India into third place by 720 PhDs. India had 24,300 PhDs in that year. 

Many PhDs live in illusion that the longer they stay in academia, the more valuable they are for industry jobs. According to an article published in The Economist, Universities are getting cheap and highly motivated PhDs. The overwhelming supply of PhDs means reduction in your demand. So don’t think that you are irreplaceable. 

Very Low Income
Gone are the days when there was an inadequate supply of postdocs, and they were highly demanded for research. Now Universities are riding on the wave of avalanche of qualified PhDs as they don’t need to pay high salaries. Due to surge in postdocs, a significant reduction in salary has been noted down. Studies have discovered that when it comes to applying in industry jobs, PhDs get few percentage higher salary than other degree holders. So lingering over academia is not worth your weight in gold. It’s not going to increase the chances of selection in industries. So leave academia, and instead start expanding your network and applying industrial jobs.

This will cost your money, time, and experience
Every minute you spend in academia after qualifying your PhD, you cost your money, time, and experience. If you applied in an industry, you would be getting higher salary. According to NIH guidelines, every postdoc get salary hike with a handsome amount every year. This is because they are gaining experience and knowledge. There is no benefit of staying in academia. You will feel like being stuck in a rut. So move to an industry to gain experience. In fact, industry PhDs are more valued than academia PhDs. 

You will lose value in academia over time
You have become a PhD and have joined academia. You will be proud while doing this, but don’t forget that every year a glut of PhDs is rising. Now what does that mean? It means that every year your worth of being in academia is going down, and if the supply of PhDs keep rising, your value will be almost zero. Each year PhDs are being devalued as many PhDs are qualified every year, and most of them opt for academia. Therefore you should focus on developing your transferable skills and apply in industrial jobs.