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Pros and Cons of Inferential Analysis

Pros and Cons of Inferential Analysis

Pros and Cons of Inferential Analysis

Inferential analysis has advantages and disadvantages. It involves deriving conclusions using random samples from the collected populace. The method is time-saving because it only utilizes a proportion of the data collected. It allows the analyst to generalize, thus providing more flexibility to the analysis process. It also enables the analyst to collect data from a more diversified population, thus involving the responses of a large population. The main disadvantage of inferential analysis is that it derives conclusions from a small sample and thus ignores most of the responses. This reduces the accuracy of the analysis due to the generalization of responses. It is also prone to sampling bias since the analyst randomly picks the samples. Thus, the analyst may favor samples with a certain similarity, thus undermining the inclusivity of the populace and the accuracy of the information generated (Andereck, 2017). Therefore, despite the flexibility and time-saving inferential analysis, creates room for bias and generalization of collected data.

Pros and Cons of Qualitative Analysis

Qualitative analysis has various advantages and disadvantages. It analyzes qualitative data such as audio records, filled questionnaires, and other written non-numerical data. Qualitative analysis is simple since it does not utilize mathematical and scientific formulas and has few variables. Thus, it requires little specialization and scientific knowledge to complete. It also uses more details, thereby increasing the accuracy of derived information. However, qualitative analysis is time-consuming because it involves the analysis of bulk records. For example, the analyst can spend hours listening to audio files and reading filled-out questionnaires. The bulk of the details is also a disadvantage because essential nuances can lead to inaccurate conclusions. The analyst may overlook the details while trying to save time and effort in the analysis. Lastly, the analysis can be affected by personal bias, especially in narrative analysis. The analyst may interpret the data from their perspective rather than the provided details (Staller, 2015). Therefore, qualitative analysis can be simple but involves a lot of time wastage and accumulation of errors.

In my research I used both inferential analysis and qualitative analysis.



Andereck, K. L. (2017). Inferential analysis of data. Research methods for leisure, recreation and tourism, (Ed. 2), 269-283.

Staller, K. M. (2015). Qualitative analysis: The art of building bridging relationships.

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Yanay Lara Corrales



In this concerned study, we are conducting a qualitative analysis. The qualitative analysis relies on subjective judgment to assess the research topic’s worth, or prospects based on non-quantifiable data. For example, when done on companies, it includes analysis on industry cycle, managerial skill, R&D, and labor relations. (SMITH, 2021). Quantitative analysis, on the other hand, is concerned with figures contained in documents such as statistical analysis.

Additionally, the two methodologies are frequently combined to investigate various research topics and assess their prospects. Many qualitative researchers would claim that gut feelings have a place in the process. Qualitative analysis might sound a lot like “listening to your gut,” that isn’t to say that it isn’t a systematic approach. It can take a lot more effort and time than quantitative analysis.

Qualitative research is more hands-on and focuses on capturing peoples’ emotions and perspectives. This has undeniable benefits, but it can also provide a slew of additional challenges beyond merely collecting quantitative data. Here are a few things to think about in terms of problems and benefits(Vaughan, 2021)


●     Qualitative research can track shifting views among a target group, such as good or service customers or opinions in work.

●     Quantitative research methods have a few constraints that can be addressed through qualitative research methods. If the replies do not match the scholar’s expectations, qualitative data can provide context and possibly explain something that numbers cannot disclose.


●     If participants have opposing goals, then it may result in simple bias.

●     Fake nature of qualitative data collection. The act of gathering a group together is inherently outside of the conventional norms of regular work life and society. And it may have unintended consequences for the participants.

Inferential analysis

Inferential statistical analysis entails summarizing the data objectively and quantitatively, deciding whether data patterns are significant, and drawing inferences about system performance. Inferential statistics provide the tools needed to discover crucial components and determine how generalizable certain test results are to the entire system. (IDAADMIN, APRIL 26, 2015)

Similar metrics like means, mode, and standard deviation are used in the inferential analysis. But the goal is distinct from qualitative analysis.

Inferential analysis strength enables the researcher to make broad generalizations about a dataset, or in most cases, a series of the database. The fundamental disadvantage is that the entries dataset is not thoroughly measured, making it impossible for a researcher to determine the conclusion.

From the above definition, we can conclude that every analysis has its assumption. Researchers must choose what better suits their research. We would research by using qualitative analysis; qualitative analysis has certain drawbacks and certain advantages, but its advantages are more than its drawbacks.


Vaughan, T. (2021, August 5th). 10 Advantages and Disadvantages of Qualitative Research. Retrieved from popular:


SMITH, T. (2021, April 22). Qualitative Analysis. Retrieved from Investopedia:

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Edited by Yanay Lara Corrales on Nov 16, 2021, 6:18:30 PM















Ivette Fernandez


Quantitative Analysis

This study will involve both inferential and qualitative content analysis as the primary methods of analyzing the data. Inferential analysis is critical for this study because it will provide actionable information that will help stakeholders implement inpatient care transition for quality patient outcomes (Edwards, 2020). This data analysis approach is practical because it supports drawing conclusions based on specific explorations and explanations, thus making it more functional. Similarly, qualitative content analysis is instrumental for this research study because it helps gather a variety of content to allow for a deeper and more organized individualized data analysis.

On the same note, the qualitative analysis looks directly into the available texts and pieces of literature based on their relevance, validity, and the qualifications of the publishers (Kalu, F. A., & Bwalya, 2017). The qualitative analysis focuses on the central aspects of social interaction, thus providing valuable historical and cultural insights. Studies have also established that qualitative analysis is relatively cheaper as compared to other methods. This is due to the availability of several research articles and journals to use as sources of data. In this regard, the researcher has a variety of data that they can use for the research study.

One of the critical disadvantages of qualitative analysis is that the study’s findings cannot be extended to the broader population even if they had the same degree of certainty. The results cannot be tested whether they are statistically significant (Kalu, F. A., & Bwalya, 2017). Most importantly, the data is non numerical and is based on the individual observations of the researcher, which can be prone to biases (Queirós, Faria, & Almeida, 2017). It is also crucial to note that this approach to data analysis is more time-consuming than other types of data analysis.



Edwards, A. (2020). Qualitative designs and analysis. In Doing early childhood research (pp. 155-175). Routledge.

Kalu, F. A., & Bwalya, J. C. (2017). What makes qualitative research good research? An exploratory analysis of critical elements. International Journal of Social Science Research, 5(2), 43-56.

Queirós, A., Faria, D., & Almeida, F. (2017). Strengths and limitations of qualitative and quantitative research methods. European Journal of Education Studies.


Week 12: Discussion Post

Student Name: Mayra Oliva Rivero




Inferential Analysis and Qualitative Analysis

What type of analysis are you conducting in your research studies?

The type of analysis I am conducting in my research studies is inferential analysis.

Advantages of inferential analysis

The inferential analysis enables analysts to extrapolate results to a more significant population. It can identify what can happen and what tends to occur in programs. Inferential analysis plays an essential role in determining the strength of the link between dependent and independent variables (Dousti, Ramchandani & Chiappelli, 2011). Finally, it permits researchers to make conclusions depending on extrapolations and so differs significantly from descriptive statistics, which report the data that has been evaluated.

Disadvantages of inferential analysis

The key disadvantage is that the whole dataset is not thoroughly measured; hence a researcher cannot be sure of the findings. The second disadvantage is that inferential statistics need the researcher to make informed estimates to execute the inferential tests.

Advantages of qualitative analysis

The qualitative analysis technique allows researchers to learn why individuals make the decisions they do daily. This will enable researchers to observe how people’s lives are organized, allowing us to create relevant content for practical implementation. Because of the low sample sizes used in qualitative analysis, it is done quickly. This structure enables researchers to swiftly collect data from participants, resulting in a generalization that can subsequently be applied to demography or the wider population (Hennink, Hutter & Bailey, 2020). Due to the high quality of the information collected, researchers may go forward with confidence due to the speedier findings. When researchers utilize qualitative analysis to focus on the issues or decisions that individuals encounter daily, it becomes feasible to identify solutions within that data that may assist everyone in solving difficulties. Rather than focusing just on decisions or actions, this method allows researchers to comprehend the context of what is happening.

Disadvantages of qualitative analysis

The data that researchers collect is always subjective. Some researchers would always think that some essential aspects are more important than others to their results. It is conceivable for different researchers to see an incident in the same area and then receive different viewpoints via qualitative analysis. Because there is a considerable amount of information accessible from the qualitative analysis method, it requires time to filter through the pieces to identify what is valuable and which is not. Qualitative analysis is unconcerned with percentages or statistics, and it needs to discover commonalities (Silverman, 2020). The prospects made feasible by qualitative research are made possible by researchers’ industry-related experience. Since qualitative analysis relies on a lesser sample size to generate a rich data profile, the intricacy of the questions involved might be an issue.




Dousti, M., Ramchandani, M. H., & Chiappelli, F. (2011). Evidence-Based Clinical Significance in Health Care: Toward an Inferential Analysis of Clinical Relevance. Dental Hypotheses, 2(3).

Hennink, M., Hutter, I., & Bailey, A. (2020). Qualitative research methods. Sage.

Silverman, D. (Ed.). (2020). Qualitative research. Sage.

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