Statistical methods are used to answer questions about the world around us. They can help us understand how nature works, and they are used to predict how things will behave in the future. You can use statistical methods to monitor a population’s health or to figure out the market demand for a certain product.
In this post, we will cover what statistical methods are and how you can apply them to solve a variety of problems. Understanding statistics and how to effectively incorporate them into your research and statistical methods and analyses are essential for handling large data volumes.
What is Statistical Data Analysis
Statistics are used to analyse data that can describe, summarise, and compare. Statistical data analysis involves the calibration of data which involves performing a range of functions involving the collection of data, interpretation of data and also finally, the validation of the data. Numerous statistical methods are used in the analysis process, with different types of statistical methods essential to get the most from the data.
Statistics is a branch of science incorporating:
- Data acquisition
- Data interpretation
- Data validation
In the context of business applications, statistical data analysis is an essential technique for handling large volumes of data and ensuring they are interpreted and used properly for the benefit of the business.
Statistical data analysis is commonly used to identify trends, for example, data sets from consumers of a particular brand could be used to identify patterns in consumer spending and inform future decisions on product choice or availability of particular stock lines.
Statistical methods are essential for getting the most from the data and essential in the fields of market research, data analysis in big data, machine learning, economic analysis, and business intelligence. Here we’re exploring basic statistical methods and the treatment of data through statistics.
Types of Statistical Data Analysis
There are two main statistical data analysis types used for analysing all kinds of data:
Descriptive statistics are used to describe, show, or summarise data for any given sample in a meaningful way. The statistical methods used for this type of analysis include mean, median, standard deviation, and variance. Descriptive analysis attempts to define the relationship between variables in data sets and provides a summary in one of the mentioned forms.
Inferential statistics make conclusions from data through the null and alternative hypotheses, both subject to random variation. As the name suggests, inferential statistics are used to infer conclusions from the framed hypotheses.
What is Statistical Treatment of Data?
Statistical treatment of data is when any form of statistical method is applied to data set to transform the data in its raw form into meaningful results and information which can be further analysed and interpreted.
Statistical treatment of data involves many different statistical methods including;
- Mean, Mode and Median
- Conditional probability
- Standard deviation
- Distribution range
Statistical treatment of data allows it to be organised and processed effectively. Conclusions can be drawn or at least considered while the data is being analysed and it allows for more in-depth and focused understanding of subgroups within the data, as well as the data for a more general interpretation. Understanding the key statistical methods for data analysis is essential to ensure the right methods are used to get the most from the data.
Key Statistical Methods for Data Analysis
The importance of data analysis and interpretation goes beyond the realm of data science as big data has become a buzz word generally in business and economic fields. The statistical methods used in data analysis will vary dependent on the sample, but these five methods below are tried, tested and effective in analysing data in sets of all sizes.
The term “mean” is more commonplace than most of those used in statistical methods. It simply means “the average” and is the sum of any list of numbers in a data set divided by the number of items on the list. The mean is useful for giving an overall trend of any data set and gives an instant snapshot of what the data shows. It’s quick to calculate and ideal if you are publishing early findings or trends before deeper analysis.
The mean is not enough alone to give a good picture of the data’s trends and meaning and should be used alongside other statistical methods such as the media and mode. Further methods from this list should also be considered for a fuller picture.
Regression models the relationships existing between dependent and explanatory variables within a data set. It is usually plotted via scatter graph. The regression line on the graph designates whether relationships between variables are strong or weak and is used regularly in scientific and business applications.
Regression is one of the least nuanced statistical methods, as the plotting of the line can tempt analysts and scientists to ignore or undervalue the importance of outlying data points.
3. Standard Deviation
The standard deviation, often simply shown as the symbol sigma (∑) from the Greek Alphabet, is the measure of a spread of data around its mean. High standard deviation means the data is spread more widely from its mean, while low standard deviation means a higher proportion of the data aligns with its mean. Standard deviation is effective for quickly determining the dispersion of the data set and its individual points.
Like other statistical method choices, standard deviation is not enough on its own and should be taken alongside other methods to ensure a full picture.
4. Sample Size Determination
Measuring large data sets or populations doesn’t require information from every single member of said group. A sample provides enough information for the hypotheses being explored.
However, you do need to determine the right size of sample and using proportion and standard deviation methods, it is possible to determine an accurate sample size to ensure data collection is statistically significant and results will be demonstrative of the wider group or population.
5. Hypothesis Testing
Commonly known as the t-test, hypothesis testing assesses if a predetermined idea is true for your specific data set. When carrying out t-testing you consider whether the result of the test is statistically significant if the results could on tave occurred by random chance. This type of test is popular for scientific applications as well as in business, research, and economics.
For t-testing to be effective, researchers and data scientists must be aware of common errors, as well as considering random errors. Common errors include the placebo effect and the Hawthorne effect. The first occurs when participants falsely expect a positive result and therefore perceive or attain this result. The second is when results are skewed because participants know they are being watched. Anyone carrying out hypothesis testing needs to keep this in mind.
Application of Statistical Analysis
Understanding what statistical methods is and what options are available is vital in ensuring data is handled in the best possible way. These methods of data analysis expand and add insight to your decision-making options and allow data to be used in a wider number of ways.
All statistical methods can be applied to your chosen data set, but it makes sense to consider the most effective for your needs and to ensure that no single method is used in isolation, as none will provide a full analysis of the data alone.
Factors which will influence your choice of statistical methods include size of data set, purpose of analysis and the type of data collected. Once you have applied your chosen methods your data will become much more accessible, presentable and be something you can translate into meaningful information for your intended purpose.