5-3 Journal: Data Analysis Plan

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The research question I am pursuing for my final project is: does the utilization of social media negatively impact academic achievement? To answer this question, I will use a survey and questionnaires to collect data from participants about their social media habits and academic performance.

When drawing a statistical conclusion, a potential Type I error would be a false positive, where a researcher incorrectly rejects the null hypothesis. This error occurs when the researcher assumes a relationship between two variables when in fact, there is none. For example, if a researcher incorrectly concludes that the utilization of social media negatively impacts academic achievement when in reality, the usage of social media has no effect on academic achievement.

On the other hand, a potential Type II error is a false negative, where a researcher incorrectly accepts the null hypothesis. This error occurs when the researcher assumes no relationship between two variables when in fact, there is one (Freiman,.2019) For example, if a researcher incorrectly concludes that the utilization of social media does not impact academic achievement when in reality, the usage of social media does in fact have an effect on academic achievement.

It is important for researchers to understand these errors because they can lead to inaccurate results, which can affect the validity of the research. It is essential for researchers to understand the consequences of making such errors in order to avoid them and produce accurate results. Additionally, understanding the implications of these errors can help researchers understand the importance of conducting proper research that is based on valid and reliable data. Understanding Type I and Type II errors can also help researchers determine the best way to collect data and analyze it in order to draw meaningful and accurate conclusions. Ultimately, understanding these errors can help researchers produce better research papers with higher quality and more accurate results.

One way to reduce the likelihood of committing Type I and Type II errors is by using a larger sample size. When conducting a survey or using questionnaires, researchers should strive for a sample size that is large enough to accurately represent the population of interest. This will help to ensure that the results will be reliable and that any errors that may arise will be minimized. Additionally, researchers should use a variety of methods to collect data, such as interviews, focus groups, and even online surveys. This can help to reduce potential biases in the data and ensure that the results are valid. Furthermore, researchers should use a variety of statistical tests to ensure that the data is analyzed correctly and that any errors are minimized (Manna,.2020)

Overall, it is important for researchers to understand Type I and Type II errors and to take the necessary steps to reduce the likelihood of committing them. Understanding these errors can help researchers to produce accurate and valid research papers with higher quality results. Additionally, understanding Type I and Type II errors can help researchers to understand the importance of conducting proper research with valid and reliable data. Ultimately, understanding these errors can help researchers to make more informed decisions and draw meaningful and accurate conclusions.

References

Freiman, J. A., Chalmers, T. C., Smith, H. A., & Kuebler, R. R. (2019). The importance of beta, the type II error, and sample size in the design and interpretation of the randomized controlled trial: survey of two sets of “negative” trials. In  Medical uses of statistics (pp. 357-389). CRC Press.

Manna, A. K., Dey, J. K., & Mondal, S. K. (2020). Effect of inspection errors on imperfect production inventory model with warranty and price discount dependent demand rate.  RAIRO-Operations Research,  54(4), 1189-1213.