On this page: What is statistical analysis? Definition and explanation. What are the different types of statistics? What is Statistical Analysis? Here are some of the fields where statistics play an important role: Market research, data collection methods , and analysis Business intelligence Data analysis SEO and optimization for user search intent Financial analysis and many others. There are two key types of statistical analysis: descriptive and inference.
The Two Main Types of Statistical Analysis In the real world of analysis, when analyzing information, it is normal to use both descriptive and inferential types of statistics.
What is descriptive and inferential statistics? What is the difference between them? Descriptive Type of Statistical Analysis As the name suggests, the descriptive statistic is used to describe!
In addition, it helps us to simplify large amounts of data in a reasonable way. Inferential Type of Statistical Analysis As you see above, the main limitation of the descriptive statistics is that it only allows you to make summations about the objects or people that you have measured.
It is a serious limitation. This is where inferential statistics come. Other Types of Statistics While the above two types of statistical analysis are the main, there are also other important types every scientist who works with data should know.
Predictive Analytics If you want to make predictions about future events, predictive analysis is what you need. Causal Analysis When you would like to understand and identify the reasons why things are as they are, causal analysis comes to help. Causal analysis searches for the root cause — the basic reason why something happens. To investigate and determine the root cause. To understand what happens to a given variable if you change another.
The purpose of exploratory data analysis is: Check mistakes or missing data. Discover new connections. Collect maximum insight into the data set.
Check assumptions and hypotheses. Mechanistic Analysis Mechanistic Analysis is not a common type of statistical analysis. About The Author Silvia Valcheva Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. 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. Normal distribution or Gaussian distribution Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point. Figure 2. Skewed distribution It is a distribution with an asymmetry of the variables about its mean. Figure 3. Curves showing negatively skewed and positively skewed distribution. Inferential statistics In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population.
Table 3 P values with interpretation. Table 4 Illustration for null hypothesis. Parametric tests The parametric tests assume that the data are on a quantitative numerical scale, with a normal distribution of the underlying population. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations. Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.
Statistical analysis is the main method for analyzing quantitative research data. It uses probabilities and models to test predictions about a population from sample data. Descriptive statistics summarize the characteristics of a data set.
Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population. Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics.
It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance. In statistical hypothesis testing , the null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.
Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test. Significance is usually denoted by a p -value , or probability value. Statistical significance is arbitrary — it depends on the threshold, or alpha value, chosen by the researcher. When the p -value falls below the chosen alpha value, then we say the result of the test is statistically significant. Have a language expert improve your writing.
Check your paper for plagiarism in 10 minutes. Do the check. Generate your APA citations for free! APA Citation Generator. Home Knowledge Base Statistics. A step-by-step guide to statistical analysis Statistical analysis means investigating trends, patterns, and relationships using quantitative data.
Example: Causal research question Can meditation improve exam performance in teenagers? Example: Correlational research question Is there a relationship between parental income and college grade point average GPA? Experimental Correlational Example: Experimental research design You design a within-subjects experiment to study whether a 5-minute meditation exercise can improve math test scores.
Your study takes repeated measures from one group of participants. Experimental Correlational Example: Variables experiment You can perform many calculations with quantitative age or test score data, whereas categorical variables can be used to decide groupings for comparison tests. A parametric correlation test can be used for quantitative data, while a non-parametric correlation test should be used if one of the variables is ordinal.
You contact three private schools and seven public schools in various districts of the city to see if you can administer your experiment to students in the 11th grade. What is your plagiarism score? Compare your paper with over 60 billion web pages and 30 million publications. Experimental Correlational Example: Descriptive statistics experiment After collecting pretest and posttest data from 30 students across the city, you calculate descriptive statistics.
Because you have normal distributed data on an interval scale, you tabulate the mean, standard deviation, variance and range. Pretest scores Posttest scores Mean Experimental Correlational Example: Paired t test for experimental research Because your research design is a within-subjects experiment, both pretest and posttest measurements come from the same group, so you require a dependent paired t test.
Since you predict a change in a specific direction an improvement in test scores , you need a one-tailed test. The test gives you: a t value test statistic of 3. The t test gives you: a t value of 3.
Experimental Correlational Example: Interpret your results experiment You compare your p value of 0. Since your p value is lower, you decide to reject the null hypothesis, and you consider your results statistically significant. What is statistical analysis? EDA can be approached for discovering unknown associations within data, inspecting missing data from collected data and obtaining maximum insights, examining assumptions and hypotheses.
In the IT industry , this is used to check the quality assurance of particular software, like why that software failed, if there was a bug, a data breach, etc, and prevents companies from major setbacks. Among the above statistical analysis, mechanistic is the least common type, however, it is worthy in the process of big data analytics and biological science.
It is deployed to understand and explain how things happen rather than how specific things will take place ulteriorly. For example, in biological science, when studying and inspecting how various parts of the virus are affected by making changes in medicine. Besides the above statistical analysis types, it is worth discussing here that these statistical treatments, or statistical data analysis techniques , profoundly rely on the way, the data is being used.
While counting on the function and requirement of a particular study, data and statistical analysis can be employed for many purposes, for example, medical scientists can use a variety of statistical analysis for testing the drug effectiveness, or potency. Also, in some cases, statistical analysis can be approached to accumulate information regarding the preference of people and their habits.
For example, user data, at sites like Facebook and Instagram , can be used by analysts for understanding user perception, like what uses are doing and what motivates them. This information can benefit commercial ads where a particular group of users are targeted to sell them things. A deeper understanding of data can widen the numerous opportunities for a business, with the implementation of business analytics , an organization can achieve while scrutinizing data, for driving, for example, predictions, insights, or conclusions from data and this is what statistical analysis can do, for example;.
And hence, a business can take advantages of statistical analysis in various ways, for example, to determine the down performance of sales, to uncover trends from customer data, conducting financial audits, etc. Check out my other blog on Bayesian Statistics. Be a part of our Instagram community. Thanks for sharing information about statistical analysis.
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