Science Statistic Analysis Tips for Academic Writing

Statistical analysis is of paramount importance to almost any type of rigorous scientific research. Data analysis is a challenging task as it provides insight into the correlation between variables. As well as identifies current trends and allows us to make predictions regarding future ones.

Statistics is used in many disciplines, including science, economics, politics, psychology, and others. For science, statistics is especially important as it allows academic researchers to identify prospective trends in drug development, make conclusions regarding the course of chemical reactions, or outline factors that most likely contribute to the stability of new industrially important substances. Statistical analysis is key for driving innovation and reducing production costs, ultimately, promoting the development of our society.

This article will outline different types of statistical analysis, describe the most common tests, and outline the software packages that can be used for the respective statistical research. Plus, we will give you plenty of tips if you need technical and statistical analysis help in your academic writing.

Tip 1: Establish the nature of the data

Qualitative vs quantitative data

Experimental scientific data may come in different forms. Qualitative research aims to establish why an event is happening. Also, as the name suggests, qualitative parameters such as words, symbols, and observations, are collected as the primary data type. This data is usually collected in the form of survey answers or any other types of data collection tools that presume open-ended questions or opinions.

Once the data is collected, the findings are interpreted based on the determined general themes. In the case of qualitative research, data is not collected in the form of specific numbers that describe events or observations. Therefore, it is not possible to apply statistical methods to analyse this type of data.

In contrast to qualitative research, quantitative analyses aim to measure the progress of an event or its result in the form of numbers. As a result, its purpose is to quantify scientific observations by using specific tests and measurements. Consequently, because the results of this type of research are presented in the form of numbers, statistical methods can be applied and the respective connections between the tested parameters/variables investigated.

It should be noted, however, that qualitative data can be transformed into quantitative with the following statistical analysis. To make the respective transformation, you must associate the collected qualitative data with numbers. However, this association may be subjective and influenced by the opinions of the researcher, introducing bias.

Tip 2: Decide on the software package that can be used

Programs for statistical analysis in science

Statistical analysis presumes the collection of a considerable amount of data. To avoid mistakes, save time, increase efficiency, and reduce research efforts, various statistical programs are available on the market. Here are some of the most important and common software:

IBM SPSS (Statistical Package for the Social Sciences)

SPSS was originally developed for social sciences. However, with numerous updates, its application has been expanded to other fields. It is commonly used in scientific research, education, marketing, and healthcare. The program can work with various types of data, including survey results, databases, Google Analytics spreadsheets, and others that presume structured numerical information. However, SPSS can not analyse big sets of data, which may be its biggest limitation.

Minitab

Similarly to SPSS, Minitab can be used to establish current and future trends based on the provided quantitative data. It can establish relationships, predict patterns, and make visualisations of the generated statistical findings. While SPSS is primarily used in social sciences and medical research, Minitab is employed to analyse theories in computer science, material science and engineering. The program is more expensive than other statistical packages. It also may be difficult for beginner users, underlining the necessity of exploring possible alternatives.

MS Excel

MS Excel is distributed as part of MS Office. It has a range of statistical functions such as “Average”, “Max”, “Min”, “Sum”, “Count”, and “Standard Deviation”. The program is easy to use and is likely already installed on a Windows-based PC, making it much more available than, SPSS and Minitab. However, the number of statistical tests is lower compared to the previously discussed packages.

Tip 3: Decide on the type of statistical test

The type of statistical tests will depend on the number of the involved variables and the question investigated. Common statistical tests used in scientific research are T-tests, ANOVA, and multiple regression.

T-tests

The purpose of the T-test is to determine if the difference between the means of two groups is statistically significant. Consequently, it requires two variables, one independent (or categorical) and one dependent (or continuous) variable.

ANOVA

This test is also known as the analysis of variance. It is employed to compare the means of at least three groups of variables to determine if at least one is statistically significantly different from the other two. In contrast to T-tests, categorical and continuous variables are used by ANOVA as independent and dependent variables.

Multiple regression analysis

Multiple regression analysis is also heavily employed in scientific research. Its purpose is to understand the relationship between two or several independent variables and one dependent variable. The purpose of this test is to develop a model which would predict the independent variables based on the dependent ones. An example of a multiple regression analysis application in science is the exploration of the climate change effects on crop yields. The independent variables can be the amount of rainfall, carbon dioxide levels, and average temperature, while the crop yield is the dependent variable.

Conclusions

Statistical scientific analysis includes a substantial number of tests that can be carried out using various software packages. The number of theories, correlations and connections which can be explored is also limitless. As a result, possible application areas may range from social sciences and psychology to industry and product design. Because the outlined applications are critical to the development of the modern economy it is expected that statistical analysis professionals will be in high demand in future.

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