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Common statistical tools used in research and their uses Jul. Self Improvement Technology. Norhac Kali Follow. Commonly Used Statistics in Survey Research. Group dynamics.
These tests examine whether one instance of sample data is greater or smaller than the median reference value. Therefore, it is useful when it is difficult to measure the values. Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.
It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other. The two-sample Kolmogorov-Smirnov KS test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.
The Kruskal—Wallis test is a non-parametric test to analyse the variance. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.
In contrast to Kruskal—Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal—Wallis test. The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.
Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups i.
It is calculated by the sum of the squared difference between observed O and the expected E data or the deviation, d divided by the expected data by the following formula:.
A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables.
It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. The null hypothesis is that the paired proportions are equal.
The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used. Numerous statistical software systems are available currently. There are a number of web resources which are related to statistical power analyses.
A few are:. It gives an output of a complete report on the computer screen which can be cut and paste into another document. It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article.
Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines. National Center for Biotechnology Information , U. Journal List Indian J Anaesth v.
Indian J Anaesth. Zulfiqar Ali and S Bala Bhaskar 1. Author information Copyright and License information Disclaimer. Address for correspondence: Dr. E-mail: moc. This article has been corrected. See Indian J Anaesth. This article has been cited by other articles in PMC. Abstract Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings.
Key words: Basic statistical tools, degree of dispersion, measures of central tendency, parametric tests and non-parametric tests, variables, variance.
Open in a separate window. Figure 1. Quantitative variables Quantitative or numerical data are subdivided into discrete and continuous measurements. Table 1 Example of descriptive and inferential statistics. Descriptive statistics The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.
Measures of central tendency The measures of central tendency are mean, median and mode. The variance of a sample is defined by slightly different formula: where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The SD of a sample is defined by slightly different formula: where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample.
Table 2 Example of mean, variance, standard deviation. The quality improvement center is statistical thinking that reflects the capability of decision-making that depends on data. Statistical thinking depends on three main concepts, that are:. Insurance is a vast industry. There are hundreds of insurance i.
The premium of insurance is based on the statistics. Insurance companies use the statistics that are collected from various homeowners, drivers, vehicle registration offices, and many more. Statistics are widely used in consumer goods products. The reason is consumer goods are daily used products.
They also used stats to find out which store needs the consumer goods and when to ship the products. Even proper statistics decisions are helping the business to make massive revenue on consumer goods.
Suppose a business organization wants to find out the number of potential customers in a specific area, their income level and preferences, statistics help them to do so. Apart from this, any business organization can recognize its competitors and trends in the market. Below is the image that helped the Indian government to make better policies related to consumer goods. The financial market completely relies on the statistical figures.
All the stock prices calculate with the help of statistics. It also helps the corporate to manage their finance to do long-term business. For instance, you want to buy the shares of a company, and you do not have an idea about the company, is it good or bad. Different types of statistical calculations such as price to book ratio, price to sale ratio, and price to earnings growth ratio will help you to invest in the right stocks.
Several tools are used for business statistics, which are built on the basis of the mean, median, and mode, the bell curve, and bar graphs, and basic probability. These can be employed for research problems related to employees, products, customer service, and much more.
The business use statistics to calculate which consumer goods are available in the store or not. We can not overlook the uses of statistics in the transportation field. Statistics results regarding transport roadways, waterways, and airways help the government and the nation to build new roads, bridges, waterlines, and airports.
With the help of statistics, the government can realize where they need to spend the money. They analyze the statistics problems to decide whether they need to construct new infrastructures or mend the old ones.
Statistical data is always useful for making new policies related to transportation. The below image is helpful to the government for making new transportation rules and regulations.
Statistics play an important role in the booming cryptocurrency field. Cryptocurrency is a digital currency.
We pay for the goods and services in the form of bitcoins. And determine the exact price of bitcoin is a difficult thing here. Cryptocurrency has a bright future, and it seems that many investors will invest in this platform in the future. So to evaluate the right price of the bitcoins, statistics is used here. Below is an example of how to use the statistics concept of maximum likelihood to calculate the standard error of the cryptocurrency.
Tourism contributes to the GDP of any nation. All the countries can generate revenue through tourism. The statistics used in tourism to find out the number of arrivals, departure, expenditure by the tourist, fatal accidents, facilities, etc.
The statistics can calculate all these factors. In addition, statistics help to improve tourism and boost the economy. Here are the statistics of how the covid had impacted the hotel occupancy rate in These statistics help to calculate the market revenue of the USA through tourism.
Statistics is essential for all sections of science, as it is amazingly beneficial for decision-making and examining the correctness of the choices that one has made. If one does not understand statistics, it is not possible to know the logical algorithms and find it challenging to develop them. Besides this, they focus on machine learning, especially data mining discovering models and relationships in information for several objectives, like finance and marketing.
Statistics has various uses in the field of robotics. Various techniques can be applied in this field, such as EM, Particle filters, Kalman filters, Bayesian networks, and much more. With the help of new input sensories, the robots continuously update themselves and give priority to the current actions.
Reactive controllers depend on sensors to create robot control. Since the mids, a new approach has been used for this purpose: probabilistic robotics. R is an open-source programming language, and it has a severe learning curve.
It is not for beginners, and you need to have good command over coding to get started with R. It was developed at Bell Laboratory by John Chambers and colleagues.
R offers a wide range of statistics and graphical techniques, i. MS Excel is one of the best statistical tools for data analysis. It offers cutting-edge solutions to data analytics professionals. You can use it for data visualization as well as simple statistics. Moreover, it is the best statistical tool for those who want to perform basic data analysis techniques on their data.
Tableau is one of the most powerful data visualization software. The data visualization technique is widely used in data analytics. Now it is the part of Salesforce that is known for its high-end CRMs in the world.
You can create the best data representation of the massive amount of data in Tableau within a few minutes. Therefore it helps the data analyst to make quick decision making. It has enormous online analytical processing cubes, cloud databases, spreadsheets, and many more.
It also offers the drag and drop interface to the users. Thus the user needs to drag and drop the data set sheet in Tableau and set the filters as per their requirements. It is not the most popular data analysis statistics tool. But you can do the basics as well as some advanced statistical techniques using Minitab. It was developed in at Pennsylvania State University. Ryan, Thomas A. Ryan, Jr. Joiners are the creators of this tool. In this way, it will enable you to find a solid answer to the most challenging questions.
Apache Hadoop is the best and most reliable statistics tool for data science.
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