Consider this scenario:

You are a researcher investigating risk factors related to pancreatic cancer. In order to promote positive social change, it is important to collect a large enough sample size to justify making generalizations to their population out of people who have pancreatic cancer.

In this Discussion, reflect on the number of variables you plan to use and consider the impact that sample size has on generalizability.


Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.


To prepare:

  • As you consider the scenario, be mindful of the number of variables you, as the researcher, intend to use and the type of research design/analysis to be conducted.
  • Also, consider the importance of sample size to generalizability.
  • Search the internet and/or the Walden Library for information related to the risk factors associated with pancreatic cancer to complete this Discussion
  • Review the Learning Resources, specifically the Power Table in the Johnson and Christensen course text.


Post your response based on the literature from your search: What should be the minimum sample size for this study related to pancreatic cancer in order to justify making generalizations from the sample to the population? What information would you need to know in order to use the Power Table to determine an appropriate sample size?

Further, explain the possible consequences of having too small of a sample size for this study.


  • Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.
    • Chapter 10, “Sampling in Quantitative, Qualitative, and Mixed Research” (pp. 239–266)
      • In particular review “Determining the Sample Size When Random Sampling Is Used” (pp. 255-257)

sample size for a study related to pancreatic cancer

To determine the minimum sample size for a study related to pancreatic cancer, several factors need consideration:

  1. Effect size: The magnitude of the relationship between the risk factors and pancreatic cancer.
  2. Desired level of statistical power: The probability of correctly rejecting the null hypothesis when it is false.
  3. Significance level: The probability of incorrectly rejecting the null hypothesis when it is true (usually set at 0.05).
  4. Type of statistical analysis: Different analyses may require different sample sizes to achieve sufficient power.

Given the severity and complexity of pancreatic cancer, it’s crucial to have a sizable sample to draw meaningful conclusions. Generally, for complex diseases like pancreatic cancer, a larger sample size is needed to ensure representativeness and reliability of findings.

The Power Table provided in Johnson and Christensen’s text can help in determining an appropriate sample size. It involves specifying the desired level of power, significance level, and expected effect size to calculate the necessary sample size for a particular statistical test.

In this case, since pancreatic cancer is a multifactorial disease, the number of variables to consider would likely be substantial. These variables could include genetic predisposition, lifestyle factors (such as diet and smoking), environmental exposures, medical history, etc. Each variable adds complexity to the analysis and may necessitate a larger sample size to ensure sufficient statistical power.

Consequences of having too small of a sample size for this study include:

  1. Reduced statistical power: With a small sample size, the study may not have enough power to detect true effects, increasing the likelihood of Type II errors (false negatives).
  2. Limited generalizability: Findings from a small sample may not accurately represent the broader population of individuals with pancreatic cancer, limiting the applicability of the results.
  3. Increased risk of bias: Small samples are more susceptible to sampling bias, where the characteristics of the sample differ systematically from those of the population, leading to biased estimates.
  4. Unreliable conclusions: Small samples increase the variability in the data, making it difficult to draw robust conclusions or establish causal relationships.
  5. Inability to conduct subgroup analyses: With a small sample, it may not be feasible to stratify the data by different risk factors or demographic characteristics, limiting the ability to explore potential interactions or subgroup effects.

Therefore, it is imperative to carefully consider the sample size requirements and ensure that it is sufficient to yield valid and generalizable results in studies related to pancreatic cancer.

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