- You should respond to at least two of your peers by extending or adding supplementary distinctions to their posts.
- All replies should contain a minimum of 100 words/numbers or a combination of both.
- All replies must be constructive and use literature where possible.
Discussion #6
Yisel Mansilla
St. Thomas University
STA-2023-AP1
Dr.Freddy Suarez
April 20 ,2023
Statistics is one of the many technical fields that requires a lot of knowledge in mathematics; statistics can be a very challenging subject, and many students normally experience difficulties in learning the many concepts that come with the subject. In my view, we cannot just say that a certain concept in statistics is the most difficult for students to learn; however, statistics do have several concepts that are very challenging to learn. These concepts include probability, hypothesis testing, sampling distributions and inferential statistics.
Probability is a fundamental concept in statistics, and it can be challenging for some students. Probability is a measure of the likelihood of an event to occur; probability provides a set of tools for analyzing random data (Hagiwara & Hagiwara, 2021). Understanding the concepts of probability, such as conditional probability, Bayesian inference, marginal probability, independence, mutually exclusive events, and poison distribution, including others, can be challenging as it involves complex mathematical computations. Hypothesis testing forms the backbone of every research; hypothesis testing can be defined as the process of evaluating if a hypothesis is true about the population based on the sample data (Levine, 2022). In hypothesis testing, many students and researchers fail prey to either type I error or type II error.
Sampling distribution is a key concept in statistics, and it involves understanding the concepts of sample means and sample proportions. In most cases, it is not possible to investigate the whole population; thus, the need for sampling distributions that allow for selecting a sample to represent the population. Statistical inference is another important concept in statistics that students find challenging to learn. Inferential statistics is the process of making inferences about the population based on the sample data (Kaliyadan & Kulkarni, 2019). In conclusion, I believe that the most difficult concept to learn in statistics depends on the individual background, prior knowledge and learning style.
References
Hagiwara, J., & Hagiwara, J. (2021). Fundamentals of Probability and Statistics. Time Series Analysis for the State-Space Model with R/Stan, 7-21.
Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian dermatology online journal, 10(1), 82.
Levine, M. (2022). A cognitive theory of learning: Research on hypothesis testing. Taylor & Francis.
What is the most difficult concept to learn in statistics and why?
Etienne, Marth-Tonita
St. Thomas University
NUR 497: Cultural Impact in Healthcare
Dr. Milien, Cassandre
19 April, 2023
What is the most difficult concept to learn in statistics and why?
In connection to what we read, I found regression and correlation analysis to two of the most challenging concepts in statistics due to their complexity and the mathematical formulas involved. Regression is a statistical technique used to predict the value of one variable based on the value of one or more other variables (Kafle, 2019). It involves fitting a regression line to a set of data points, which requires a solid understanding of basic probability and distribution theory. The analysis can be challenging to interpret, especially when dealing with complex data sets, and may require the use of multiple regression models to account for multiple predictor variables.
Correlation analysis, on the other hand, measures the strength and direction of the relationship between two variables. It can be difficult to interpret the results of correlation analysis and understand how they relate to real-world phenomena. For example, a high correlation between two variables does not necessarily indicate causation, and other factors may be influencing the relationship between the variables. In addition, correlation analysis may be affected by outliers or other sources of variability in the data, which can further complicate the interpretation of results.
I found that both regression and correlation analysis require a solid understanding of basic probability and distribution theory, as well as advanced statistical techniques such as hypothesis testing and confidence intervals Dakhlan, 2020). The mathematical formulas involved can be complex, and students may struggle to apply these concepts to real-world scenarios. Additionally, the application of regression and correlation analysis to specific fields such as business, marketing, and behavioral science can further complicate the interpretation of results due to the unique characteristics of these data sets and the ethical considerations involved in decision-making based on statistical models.
In conclusion, regression and correlation analysis are challenging concepts in statistics that require a solid understanding of basic probability and distribution theory, as well as advanced statistical techniques. These concepts are important for predicting and understanding relationships between variables, but can be difficult to apply to real-world scenarios and may be influenced by sources of variability and ethical considerations. With patience and practice, however, students can develop a deeper understanding of these concepts and their applications.
References
Dakhlan, A., Hamdani, M., & Sulastri, S. (2020). Regression models and correlation analysis for predicting body weight of female Ettawa Grade goat using its body measurements. Advances in Animal and Veterinary Sciences, 8(11), 1142-1146.
Kafle, S. C. (2019). Correlation and regression analysis using SPSS. Management, Technology & Social Sciences, 126. http://repository.lppm.unila.ac.id/25006/
