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What is Inferential Statistics in Data Analysis

In Inferential statistics, we make an inference from a sample about the population. The main aim of inferential statistics is to draw some conclusions from the sample and generalise them for the population data. Inferential Statistics in quantitative research works in addition to Descriptive Statistics. Where descriptive statistics helps to summarise the characteristics of a sample population, inferential statistics focuses on using that summarised data and predicting the characteristics for the larger population.

 

What is Inferential Statistics?

Given a sample of data, an inference is made to discover unknown information related to the larger population. There are various inferential statistics in research, business and economics, like hypothesis testing, sampling, and probability. In hypothesis testing, data is collected and then a null hypothesis and an alternate hypothesis are made. For example, if a researcher wants to find out the percentage of people who consume a particular food item in their country, then the researcher will have to collect data about the number of people who consume that particular food item. The researcher will then hypothesise that, the percentage of people who consume that food item is more in that country compared to other countries.

 

What is Hypothesis Testing in Inferential Statistics?

Hypothesis testing is the process, where a researcher collects data from a sample from a population and then makes a null and alternate hypothesis about the population based on the sample data. The null hypothesis is, “there is no significant difference between the sampled population and the population”. Whereas, the alternate hypothesis is, “there is a significant difference between the sampled population and the population”. Let’s consider an example to understand hypothesis testing in inferential statistics better. Suppose, a researcher visits a random shopping mall and collects data about the number of people who shop at different stores in the mall. The researcher will hypothesise that the number of people who shop at a particular store in the mall is more than the number of people who shop at other stores in the mall. The researcher can then make conclusions about the mall, that all the other stores, who don’t collect such a high number of customers, have to improve their service and make their products, which are liked by the customers, more popular in the mall.

 

Various Statistical Tests in Inferential Statistics

There are various statistical tests available in inferential statistics. These statistical tests are used to make a conclusion about the population based on the sample data. Different statistical tests in inferential statistics are explained below. – Hypothesis Test – Null and Alternative Hypothesis – Chi-Square Test – Correlation Coefficient – Regression Line – Probability in Bayes’ Theorem – Uniform Random Sampling – External Validation Methodology – Considerations – Conclusion

 

Difference Between Descriptive and Inferential Statistics?

Let’s understand the difference between descriptive and inferential statistics. Firstly, descriptive statistics helps to discover unknown information related to a particular sample. It simply describes the characteristics of the sample. On the other hand, inferential statistics makes an inference about the population based on the sample data. Let’s consider an example to understand the difference between descriptive and inferential statistics. Suppose, a researcher visits a town and calls a random sample of 100 people and asks them, “What is your profession?” and “How old are you?”. The researcher simply describes the characteristics of the sample. Now, if a researcher wants to make a conclusion about the town, then inference is made from the sample data. The conclusion can be as below. “People in this town are older than average people and their profession is more than average people”. Hence, the difference between descriptive and inferential statistics is very clear in this example.

 

Conclusion

In short, inferential statistics uses the data collected from a sample to make conclusions about the population. It is entirely distinct from descriptive statistics, where the characteristics of the sample are described. Inferential statistics is widely used in business, economics, and other quantitative fields, whereas descriptive statistics is used in qualitative research.

Conceptual Framework in Your Research: Developing A Template

A conceptual framework is a hierarchical representation of the relationships between variables under study. It can be created in a number of ways, but is most commonly displayed as a tree. The nodes at the top of the tree represent the variables being studied and may include terms such as “Condition”, “Variable”, and “Main Effect”. Each node further down the tree represents a specific relationship between the variables being studied and may include terms such as “Correlation”, “Association”, and “Time Effect”. The leaves at the bottom of the tree represent the outcomes (usually measurements) that are expected to be associated with each variable under study.

The main benefit of having a conceptual framework is that it allows researchers to think through all possible relationships between variables in order to gain a better understanding of how they might influence each other. It also helps to clarify which variables are most important to measure when designing research projects. For instance, if one variable is found to have an effect on another variable, this can help researchers better understand how to best measure both variables in future studies. However, conceptual frameworks are not always necessary in all research projects. Some may only be needed for longer-term research projects or those that involve multiple groups of participants or data collection methods (e.g., qualitative versus quantitative). 

Creating a Conceptual Framework

Researchers use a conceptual framework to provide a visual or written representation of key variables, factors or concepts and their relationship with each other that will be studied in the present research. 

  • Selecting a suitable research topic is critical in creating a conceptual framework. Before you begin your research, you must determine your topic. You must choose a research topic that interests you. Remember to check for available resources before you choose a topic. It may be beneficial to look for possible research resources before choosing a subject. It is also important to determine how much time it will take to research the case and whether you will have enough time to finish your work by the deadline.
  • Your research question determines exactly what you want to find out, helping to focus your research process. This is a crucial component of your conceptual framework, since this research question will determine how you will proceed throughout the course of your research.
  • A literature review focuses on the evaluation of current and relevant literature in a particular subject area to assess one’s knowledge and understanding of the literature. It involves the exploration and assessment of available literature on a specific topic.
  • The proper selection of variables is critical to the development of a research framework. Your independent and dependent variables should be determined initially after you have completed the literature review, you must find these variables relevant to your research topic and establish key relationships between them. 
  • The conceptual framework can then be developed with reference to your problem statement and detailing out the cause and effect relationships between variables graphically.
    •  You can also proceed to establishing other influencing variables like the moderating variables that can affect the association between independent and dependent variables by strengthening or diminishing the relationship between them. 
    • The mediating variable that explains the relationship between the independent and dependent variables and is affected by changes in the independent variable and resultantly affects the dependent variable. 
    • The researcher can also establish certain control variables in this graphical representation which are presumed to be constant so that they don’t interfere with the results. They are defined in most research studies because the possibility of them occurring is high but are not studied or accounted for in the particular research. 

 

The ‘Six Rs’ model of (Waller, 2022) is quite useful as a general guideline to develop a conceptual framework for your research. 

  • Review – literature/themes/theory
  • Reflect – what are the main concepts/issues?
  • Relationships – what are their relationships?
  • Reflect – does the diagram represent it sufficiently?
  • Review – check it with theory, colleagues, stakeholders, etc
  • Repeat – review and revise it to see if something better occurs

The main idea espoused here is directly related to the elaborated steps of development as explained previously. A framework of concepts, assumptions, expectations, and beliefs is used to guide a research study when generalising from specific instances, these instances are demarcated from the literature review that the researcher carries out in pursuit of establishing connections to the research topic selected. A framework may be conceived of as a system of concepts, assumptions, expectations, and beliefs that link broad ideas or models that are created by reflecting on the main concepts or issues your research is trying to investigate. This would be an effective guide for a research study in order to establish a systematic order to the flow or logic of the study. 

The researcher utilises literature to link real-world experiences or events to shape future research thoughts or methods in their research study which is used to derive the various necessary variables and establish relationships between them  in order to construct a conceptual framework. The conceptual framework is then diagrammatically presented and assessed to understand if it espouses the core area the researcher is investigating with his or her research and with a feedback or reflection on this specific question, the framework can be further amended to satisfaction. 

Wrapping Up

The conceptual framework acts as a link between literature, methodology, and results and mostly is used in qualitative research in the social and behavioural sciences. The framework helps the researcher visualise the research and understand the key variables that will dictate the course of the study and how the relationship between the variables must be accurately applied when investigating the phenomena under study. The conceptual framework also helps the researcher in formulating the best data collection and developing suitable tests to analyse the data to find effective results. 

References:

  1. Waller, D. (2022) Chart your research with a graphical conceptual framework, LX at UTS. Available at: https://lx.uts.edu.au/blog/2022/04/12/chart-your-research-with-a-graphical-conceptual-framework/ (Accessed: 2022).
  2. Miles, M. B. & Huberman, M.A. (1994) Qualitative data analysis: An expanded sourcebook. 2nd ed. Thousand Oaks, CA: SAGE.