Statistics is an integral part of all research studies, a discipline that all researchers and scientists commonly use to analyze their data and arrive at the desired research findings and effectively present meaningful conclusions. Statistical techniques , presently, play a central part in the majority of scientific disciplines and are frequently utilized to support hypotheses in order to provide credibility to research methodology and conclusions arrived from these studies. Thus, many researchers and scholars are keenly interested In Statistics and its various methods and analytical techniques to ascertain the credibility and usefulness of the data and information collected from their respective research studies. This short guide provides essential information relating to important statistical concepts like sampling methods, the role of statistics in scientific research to facilitate them to conduct a well-designed statistical research. What is Statistics? Statistics...

Significance of Statistics in Research Being an integral part of any research related to most subjects, Statistics and its importance in research have been recognized since long. Quality research is invariably in most topic cover portions of statistics. Statistics is linked to a wide range of research activities. This is where scholars feel the need for effective  Ph.D. statistics help  for pursuing their research. Apart from subject knowledge, researchers need to have expertise in statistical methods also. For validating the findings, statistical portions are being made mandatory by institutions across the globe. It is by having theoretical knowledge as well as skill in the application of statistics that researchers will be able to gain an extra edge, making  PhD statistics help  a vital factor. “The goal is to turn data into information, and information into insight.” – Carly Fiorina, former CEO, Hewlett-Packard Co. Conducting Statistical Tests ...

Significance of Quantitative Statistical Data Analysis Statistical Data analysis in quantitative research  generally involves varied statistical techniques such as regression analysis, multivariate analysis, significance testing, and so on. These can be efficiently performed by expert analysts having quantitative skills and extensive knowledge in statistics. Data can statistically be inferred only after performing the  Quantitative Data Analysis . You have to turn raw numbers into meaningful data in  Quantitative Data Analysis  by applying critical and rational thinking. As same figure within a data-set may be interpreted in different ways, it becomes vital to apply careful and fair judgment.  Data analysis in quantitative research  must be performed by professionals having relevant experience and skill. “Statistics is the grammar of science.” Karl Pearson Quantitative Research Methods for PhD Students – the Challenges: It is usual for...

Statistical Regression analysis  provides an equation that explains the nature and relationship between the predictor variables and response variables. For a linear regression analysis, following are some of the ways in which inferences can be drawn based on the output of p-values and coefficients. While interpreting the p-values in  linear regression analysis in statistics , the p-value of each term decides the coefficient which if zero becomes a null hypothesis. A low p-value of less than .05 allows you to reject the null hypothesis. This could mean that if a predictor has a low p-value, it could be an effective addition to the model as the changes in the value of the predictor are directly proportional to the changes in the response variable. On the contrary, a p-value that is larger does not affect the model as in that case, the changes in the value of the predictor and the changes in the response variable are not directly linked. If you are to take a...

Tremendous amount of data has been collected by organizations all around the world thanks primarily to the World Wide Web! This collection is useless unless it is analyzed to see a pattern. It is important for decision makers, researchers and policy makers throughout industry, academia and Governments to integrate the huge collected data to suit their needs.  A good software  provider will seek to understand your organization’s unique needs and work with you to find the most cost-effective solution. How does one decide the best statistical software to use? There are questions of price, time available to learn to use it and most importantly the nature of data. Excel is quite a powerful instrument often used in biomedical research to combine large volumes of data. It does contain some  basic statistical analysis  and could serve for simple inferences. Data can be exported into an excel spreadsheet, making it easy to test or determine the dispersion...

Publishing a research paper in a peer reviewed journal seems to be one of the daunting tasks that require tackling multiple challenges on a technical and personal level. Researchers without prior experience in publishing would have a tough time understanding the nuances of acing journal acceptance rates for their research work. It is advisable for the early stage researchers to seek the expertise of Statistical peer reviewers who can help in incorporating the nuances to significantly improve journal acceptance rates. Read on to know how the below mentioned factors handled by the expert Statistical peer review help s in journal acceptance. The Publisher Expert peer reviewers clearly understand the acceptance knacks and specifications of each publishing house and provide suggestions to modify the research paper accordingly to reap the best results. Expectation of the audience As these statistical peer reviewers have exceptional domain knowledge and exposure to the ind...

The main purpose of this blog is to understand the Testing Group Differences using T-tests, ANOVA, and Non parametric Measures. Choosing the right test for your data analysis is a very difficult task particularly identifying the Different methods from testing group differences is the Biggest challenging task. It is important to have to in-depth Knowledge to understand and calculate T-tests, ANOVA, and Nonparametric, brief interpretation of the output. In order to choose the right statistical test, when analyzing the data from an experiment, we must have a good understanding of some basic statistical terms and concepts: Test for Normality Every data must follow certain distribution. But we have to find the appropriate distribution from goodness of fit test. So, our data is checked through each and every distribution. Hence, goodness of fit test is very tedious. This way of estimation of data is called by parametric tests. Parametric tests always give the reliable estimated...