“The number of people who think they understand statistics dangerously dwarfs those who actually do, and maths can cause fundamental problems when badly used.”― Rory Sutherland
In the information era, data is no protracted scarce, on the other hand, it is irresistible. From delving into the overpowering quantity of data to precisely interpret its complexity in order to provide insights for intense progress to organizations and businesses, all sorts of data and information is exploited at their entirety and this is where statistical data analysis has a significant part.
“Statistics is the specific branch of science from where the professionalists bring distinct conclusion/interference under the same data”
Moving discussion a step further, we shall discuss;
What is Statistical Data Analysis?
Significance of data in Statistical Data Analysis
Statistical Data Analysis Tools
What are the types of Statistical Data Analysis
4 steps process of Statistical Data Analysis
Being a branch of science, Statistics incorporates data acquisition, data interpretation, and data validation, and statistical data analysis is the approach of conducting various statistical operations, i.e. thorough quantitative research that attempts to quantify data and employs some sorts of statistical analysis. Here, quantitative data typically includes descriptive data like survey data and observational data.
In the context of business applications, it is a very crucial technique for business intelligence organizations that need to operate with large data volumes.
The basic goal of statistical data analysis is to identify trends, for example, in the retailing business, this method can be approached to uncover patterns in unstructured and semi-structured consumer data that can be used for making more powerful decisions for enhancing customer experience and progressing sales.
Apart from that, statistical data analysis has various applications in the field of statistical analysis of market research, business intelligence(BI), data analytics in big data, machine learning and deep learning, and financial and economical analysis.
(Recommend blog: Top Business Intelligence Tools and Techniques in 2020)
Data comprises variables which are univariate or multivariate, and extremely relying on the number of variables, the experts execute several statistical techniques.
If the data has a singular variable then univariate statistical data analysis can be conducted including t-test for significance, z test, f test, ANOVA test- one way, etc.
And if the data has many variables then different multivariate techniques can be performed such as statistical data analysis, or discriminant statistical data analysis, etc.
Here, the variable is a characteristic, changing from one individual trait of a population to another trait. The image below shows the classification of data-variables.
Classification of Variables, Source
(Related blog: An Introduction to Probability Distribution)
Data is of two types, continuous data and discrete data. The continuous data cannot be counted and changes over time, e.g the intensity of light, the temperature of a room, etc.
The discrete data can be counted and has a certain number of values, e.g. the number of bulbs, the number of people in a group, etc.
(Related blog: Types of data in statistics)
Under statistical data analysis,
the continuous data is distributed under continuous distribution function, also known as the probability density function, and
the discrete data is distributed under a discrete distribution function, also termed as the probability mass function.
Data can either be quantitative or qualitative.
Qualitative data are labels or names that are implemented to find a characteristic of each element, whereas
quantitative data are always in the form of numbers that intimate either how much or how many.
(More to read: Steps for qualitative data analysis)
Under statistical data analysis, cross-sectional and time-series data are important. For a definition, cross-sectional data are the data accumulated at the same time or relatively the same point in time, whereas, time-series data are the data gathered across certain time periods.
Existing essential findings/conclusions unveiled through a dataset.
Abstract and compile information.
Compute measures of cohesiveness, relevance, or diversity in data.
Originate forthcoming prophecies on the basis of earlier reported data.
Test experimental forecasts.
Generally, under statistical data analysis, some form of statistical analysis tools are practised that a layman can’t do without having statistical knowledge.
Various software programs are available to perform statistical data analysis, these software include Statistical Analysis System(SAS), Statistical Package for Social Science (SPSS), Stat soft and many more.
These tools allow extensive data-handling capabilities and several statistical analysis methods that could examine a small chunk to very comprehensive data statistics.
Though computers serve as an important factor in statistical data analysis that can assist in the summarization of data, statistical data analysis concentrates on the interpretation of the result in order to drive inferences and prophecies.
(Must check: Statistical Data analysis techniques)
There are two important components of a statistical study, that are:
Population - an assemblage of all elements of interest in a study, and
Sample - a subset of the population.
And, there are two types of widely used statistical methods under statistical data analysis techniques;
It is a form of data analysis that is basically used to describe, show or summarize data from a sample in a meaningful way. For example, mean, median, standard deviation and variance.
In other words, descriptive statistics attempts to illustrate the relationship between variables in a sample or population and gives a summary in the form of mean, median and mode.
This method is used for making conclusions from the data sample by using the null and alternative hypotheses that are subjected to random variation.
Also, probability distribution, correlation testing and regression analysis fall into this category. In simple words, inferential statistics employs a random sample of data, taken from a population, to make and explain inferences about the whole population.
(Most related: What is p-value in statistics?)
The table below shows the factual differences between descriptive statistics and inferential statistics;
Related with specifying the target population.
Make inferences from the sample and make them generalize also according to the population.
Arrange, analyze and reflect the data in a meaningful mode.
Correlate, test and anticipate future outcomes.
Concluding outcomes are represented in the form of charts, tables and graphs.
Final outcomes are the probability scores.
Explains the earlier acknowledged data.
Attempts in making conclusions regarding the population which is beyond the data available.
Deployed tools-Measure of central tendency (mean, median, mode), Spread of data (Range, standard deviation, etc.)
Deployed tools- Hypothesis testing, Analysis of variance, etc.
Difference between Descriptive Statistics and Inferential Statistics
In order to analyze any problem with the use of statistical data analysis comprises four basic steps;
The precise and actuarial definition of the problem is imperative for achieving accurate data concerning it. It becomes extremely difficult to collect data without knowing the exact definition/address of the problem.
After addressing the specific problem, designing multiple ways in order to accumulate data is an important task under statistical data analysis.
Data can be collected from the actual sources or can be obtained by observation and experimental research studies, conducted to get new data.
In an experimental study, the important variable is identified according to the defined problem, then one or more elements in the study are controlled for getting data regarding how these elements affect other variables.
In an observational study, no trial is executed for controlling or impacting the important variable. For example, a conducted surrey is the examples or a common type of observational study.
Under statistical data analysis, the analyzing methods are divided into two categories;
Exploratory methods, this method is deployed for determining what the data is revealing by using simple arithmetic and easy-drawing graphs/description in order to summarize data.
Confirmatory methods, this method adopts concept and ideas from probability theory for trying to answer particular problems.
Probability is extremely imperative in decision-making as it gives a procedure for estimating, representing, and explaining the possibilities associated with forthcoming events.
By inferences, an estimate or test that claims to be the characteristics of a population can be derived from a sample, these results could be reported in the form of a table, a graph or a set of percentages.
Since only a small portion of data has been investigated, therefore the reported result can depict some uncertainties by implementing probability statements and intervals of values.
With the help of statistical data analysis, experts could forecast and anticipate future aspects from data. By understanding the information available and utilizing it effectively may lead to adequate decision-making. (Source)
The statistical data analysis furnishes sense to the meaningless numbers and thereby giving life to lifeless data. Therefore, it is imperative for a researcher to have adequate knowledge about statistics and statistical methods to perform any research study.
This will assist in conducting an appropriate and well-designed study preeminently to accurate and reliable results. Also, results and inferences are explicit only and only if proper statistical tests are practised.
“Regression analysis is the hydrogen bomb of the statistics arsenal.”― Charles Wheelan
While concluding the blog, we can say that statistical data analysis is nothing but the compilation and interpretation of data in order to reveal hidden patterns and trends.
(Related blog: Types of Statistical Analysis)
It can be adopted in dealing with situations like accumulating research analyses, statistical modelling or sketching surveys and studies.
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