{"id":9437,"date":"2026-06-01T21:33:48","date_gmt":"2026-06-01T21:33:48","guid":{"rendered":"https:\/\/kapdec.com\/help\/?p=9437"},"modified":"2026-06-01T21:33:48","modified_gmt":"2026-06-01T21:33:48","slug":"sources-of-bias-designing-experiment","status":"publish","type":"post","link":"https:\/\/kapdec.com\/help\/sources-of-bias-designing-experiment\/","title":{"rendered":"Sources Of Bias & Designing Experiment"},"content":{"rendered":"

Unit: <\/strong>Collecting Data<\/strong><\/h2>\n

Chapter: Sources of Bias & Designing Experiment.<\/strong><\/h3>\n

Reference: – Types of Bias & their sampling methods, non-responsive bias, Minimization, Under coverage bias, Voluntary response bias, Response bias, Experimental design, Controlled Experiments, Causation vs Correlation, Sampling techniques & Validity, Ethical considerations, Bias in surveys & Experiments<\/em><\/p>\n

After studying this chapter, you should be able to:<\/strong><\/p>\n

    \n
  • Types of Bias & their Sampling methods.<\/li>\n
  • Non – responsive Bias & Their minimization<\/li>\n
  • Causation vs Correlation & Ethical considerations.<\/li>\n
  • Sampling techniques & Validity.<\/li>\n<\/ul>\n

    Bias in Sampling methods<\/strong><\/p>\n

      \n
    1. Random Sampling:<\/strong>\n
        \n
      1. Random sampling is a method where each member of the population has an equal chance of being selected for the sample.<\/li>\n
      2. It helps reduce bias by ensuring that every individual has a fair opportunity to be included.<\/li>\n<\/ol>\n<\/li>\n
      3. Systematic Sampling:<\/strong>\n
          \n
        1. Systematic sampling involves selecting every nth individual from the population.<\/li>\n
        2. It can introduce bias if there's a pattern in the population that aligns with the sampling interval.<\/li>\n<\/ol>\n<\/li>\n
        3. Stratified Sampling:<\/strong>\n
            \n
          1. In stratified sampling, the population is divided into distinct subgroups (strata) based on certain characteristics.<\/li>\n
          2. It helps ensure representation from different groups, reducing bias by preventing underrepresentation.<\/li>\n<\/ol>\n<\/li>\n
          3. Cluster Sampling:<\/strong>\n
              \n
            1. Cluster sampling involves dividing the population into clusters, then randomly selecting entire clusters for the sample.<\/li>\n
            2. Bias can arise if clusters are not representative of the population or if there's a wide variability within clusters.<\/li>\n<\/ol>\n<\/li>\n
            3. Convenience Sampling:<\/strong>\n
                \n
              1. Convenience sampling involves selecting individuals who are readily available or easy to reach.<\/li>\n
              2. This method can introduce bias because it may not represent the entire population's characteristics.<\/li>\n<\/ol>\n<\/li>\n
              3. Bias in Non-Random Sampling:<\/strong>\n
                  \n
                1. Non-random sampling methods (e.g., convenience or judgment sampling) can lead to selection bias, where certain groups are overrepresented or underrepresented.<\/li>\n<\/ol>\n<\/li>\n
                2. Voluntary Response Bias:<\/strong>\n
                    \n
                  1. Voluntary response bias occurs when individuals self-select to participate in a survey or study.<\/li>\n
                  2. The sample may not accurately represent the population because those with strong opinions are more likely to respond.<\/li>\n<\/ol>\n<\/li>\n
                  3. Under coverage Bias:<\/strong>\n
                      \n
                    1. Under coverage bias happens when certain segments of the population have a lower chance of being included in the sample.<\/li>\n
                    2. This can lead to results that don't accurately reflect the entire population.<\/li>\n<\/ol>\n<\/li>\n
                    3. Non-Response Bias:<\/strong>\n
                        \n
                      1. Non-response bias occurs when individuals selected for the sample do not respond or participate in the study.<\/li>\n
                      2. The non-respondents might have different characteristics, leading to a biased sample.<\/li>\n<\/ol>\n<\/li>\n
                      3. Random Sampling Error:<\/strong>\n
                          \n
                        1. Even with random sampling, there is a chance of random sampling error, where the sample's characteristics differ from the population's due to chance.<\/li>\n<\/ol>\n<\/li>\n
                        2. Sample Size and Bias:<\/strong>\n
                            \n
                          1. Smaller sample sizes are more susceptible to bias as chance variations can have a larger impact.<\/li>\n<\/ol>\n<\/li>\n
                          2. Sample Frame:<\/strong>\n
                              \n
                            1. The list or database from which the sample is drawn is the sample frame.<\/li>\n
                            2. A biased sample frame can lead to bias in the sample.<\/li>\n<\/ol>\n<\/li>\n
                            3. Overcoming Bias:<\/strong>\n
                                \n
                              1. Random selection and appropriate sampling methods can help mitigate bias.<\/li>\n
                              2. Stratified and cluster sampling can address biases related to specific population characteristics.<\/li>\n<\/ol>\n<\/li>\n
                              3. Bias vs. Variability:<\/strong>\n
                                  \n
                                1. Bias refers to a consistent deviation from the true value, while variability refers to how much the data points differ from each other.<\/li>\n<\/ol>\n<\/li>\n
                                2. Representativeness:<\/strong>\n
                                    \n
                                  1. The goal of sampling is to create a representative sample that accurately reflects the population's characteristics, minimizing bias.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

                                    Types of Sampling & Their Explanation<\/strong><\/p>\n

                                      \n
                                    1. Simple Random Sampling:<\/strong>\n
                                        \n
                                      • Explanation: Every member of the population has an equal chance of being selected for the sample. This is often done using random number generators or drawing lots.<\/li>\n
                                      • Example: Selecting 50 students from a school by assigning each student a unique number and then using a random number generator to pick the numbers.<\/li>\n<\/ul>\n<\/li>\n
                                      • Stratified Sampling:<\/strong>\n
                                          \n
                                        • Explanation: The population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum in proportion to its size in the population.<\/li>\n
                                        • Example: Dividing a city's population into age groups (e.g., 0-18, 19-35, 36-50, 51 and above) and then randomly selecting individuals from each age group.<\/li>\n<\/ul>\n<\/li>\n
                                        • Systematic Sampling:<\/strong>\n
                                            \n
                                          • Explanation: A starting point is chosen randomly, and then every nth member of the population is selected for the sample.<\/li>\n
                                          • Example: Selecting every 10th customer entering a store to participate in a survey about their shopping habits.<\/li>\n<\/ul>\n<\/li>\n
                                          • Cluster Sampling:<\/strong>\n
                                              \n
                                            • Explanation: The population is divided into clusters (groups or areas), and a random sample of clusters is selected. All individuals within the selected clusters are included in the sample.<\/li>\n
                                            • Example: Selecting a few schools from different districts and surveying all students within the selected schools.<\/li>\n<\/ul>\n<\/li>\n
                                            • Convenience Sampling:<\/strong>\n
                                                \n
                                              • Explanation: Individuals who are easiest to reach or are readily available are included in the sample.<\/li>\n
                                              • Example: Conducting a survey of customers who visit a store on a particular day.<\/li>\n<\/ul>\n<\/li>\n
                                              • Voluntary Response Sampling:<\/strong>\n
                                                  \n
                                                • Explanation: Individuals self-select to be part of the sample, often in response to an open invitation.<\/li>\n
                                                • Example: Setting up an online poll where people can choose to participate by clicking a link.<\/li>\n<\/ul>\n<\/li>\n
                                                • Judgmental (or Purposive) Sampling:<\/strong>\n
                                                    \n
                                                  • Explanation: The researcher uses personal judgment to select individuals who are considered representative of the population.<\/li>\n
                                                  • Example: Selecting specific patients for a medical study based on their unique medical conditions.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n

                                                    Non-Responsive Bias & their Minimization<\/strong><\/p>\n

                                                    Non-Response Bias<\/strong>:<\/p>\n

                                                      \n
                                                    • Non-response bias occurs when individuals selected for a survey or study do not participate, leading to potential distortion in results.<\/li>\n
                                                    • Underrepresentation:<\/li>\n
                                                    • Non-respondents may differ systematically from respondents, leading to underrepresentation of certain groups or perspectives.<\/li>\n<\/ul>\n

                                                      Example:<\/strong><\/p>\n

                                                        \n
                                                      • In a political survey, if supporters of a particular candidate are less likely to respond, the survey results may be biased.<\/li>\n
                                                      • Impact on Results:<\/li>\n
                                                      • Non-response bias can lead to inaccurate estimates, distorted trends, and decreased generalizability of findings.<\/li>\n
                                                      • Non-Random Non-Response:<\/li>\n
                                                      • When non-response is related to the study's variables, it can lead to bias that is difficult to correct.<\/li>\n<\/ul>\n

                                                        Causes of Non-Response:<\/strong><\/p>\n

                                                          \n
                                                        • Factors such as time constraints, lack of interest, privacy concerns, or survey complexity can contribute to non-response.<\/li>\n<\/ul>\n

                                                           <\/p>\n

                                                          Minimization Strategies:<\/strong><\/p>\n

                                                            \n
                                                          • Ensuring clear and concise survey questions can reduce respondent burden and encourage participation.<\/li>\n<\/ul>\n

                                                                 Pre-Survey Communication:<\/strong><\/p>\n

                                                              \n
                                                            • Informing potential participants about the survey's importance and confidentiality can increase response rates.<\/li>\n<\/ul>\n

                                                              Incentives:<\/strong><\/p>\n

                                                                \n
                                                              • Offering incentives like small rewards can motivate individuals to participate and reduce non-response.<\/li>\n<\/ul>\n

                                                                Follow-Up Efforts:<\/strong><\/p>\n

                                                                  \n
                                                                • Contacting non-respondents with reminders or additional survey opportunities can increase participation.<\/li>\n<\/ul>\n

                                                                  Non-Response Weighting:<\/strong><\/p>\n

                                                                    \n
                                                                  • Assigning different weights to respondents and non-respondents based on known characteristics can help correct bias.<\/li>\n<\/ul>\n

                                                                    Imputation Techniques:<\/strong><\/p>\n

                                                                      \n
                                                                    • Using statistical methods to estimate missing values based on responses from similar participants can mitigate non-response bias.<\/li>\n<\/ul>\n

                                                                      Non-Response Analysis:<\/strong><\/p>\n

                                                                        \n
                                                                      • Analyzing characteristics of respondents and non-respondents can help identify potential biases and adjust for them.<\/li>\n<\/ul>\n

                                                                        Multiple Data Sources:<\/strong><\/p>\n

                                                                          \n
                                                                        • Collecting information from various sources can provide a more complete picture and reduce reliance on a single biased sample.<\/li>\n<\/ul>\n

                                                                          Transparency and Reporting:<\/strong><\/p>\n

                                                                            \n
                                                                          • Clearly documenting non-response rates and efforts to address bias enhances the study's credibility and allows for better interpretation of results.<\/li>\n<\/ul>\n

                                                                             <\/p>\n

                                                                            Causation vs Correlation & Ethical Considerations<\/strong><\/p>\n

                                                                             <\/p>\n

                                                                            Causation vs. Correlation<\/strong>:<\/p>\n

                                                                             <\/p>\n

                                                                              \n
                                                                            1. Causation:<\/strong>\n
                                                                                \n
                                                                              • Causation implies a cause-and-effect relationship where changes in one variable directly influence changes in another.<\/li>\n<\/ul>\n<\/li>\n
                                                                              • Correlation:<\/strong>\n
                                                                                  \n
                                                                                • Correlation indicates a statistical relationship between two variables, but it does not necessarily imply a causal connection.<\/li>\n<\/ul>\n<\/li>\n
                                                                                • Third Variable Confounding:<\/strong>\n
                                                                                    \n
                                                                                  • Correlation between two variables can be influenced by a third variable that affects both, creating a spurious correlation.<\/li>\n<\/ul>\n<\/li>\n
                                                                                  • Direction of Causation:<\/strong>\n
                                                                                      \n
                                                                                    • Establishing which variable causes the other can be challenging based solely on correlation.<\/li>\n<\/ul>\n<\/li>\n
                                                                                    • Reverse Causation:<\/strong>\n
                                                                                        \n
                                                                                      • In some cases, the causation might be reversed, meaning changes in one variable are caused by changes in the other.<\/li>\n<\/ul>\n<\/li>\n
                                                                                      • Coincidence:<\/strong>\n
                                                                                          \n
                                                                                        • Correlation can sometimes occur by chance, leading to a false perception of causation.<\/li>\n<\/ul>\n<\/li>\n
                                                                                        • Experimentation and Causation:<\/strong>\n
                                                                                            \n
                                                                                          • Well-designed experiments can provide stronger evidence of causation by controlling variables and randomizing treatments.<\/li>\n<\/ul>\n<\/li>\n
                                                                                          • Observational Studies:<\/strong>\n
                                                                                              \n
                                                                                            • Observational studies may reveal correlations but cannot definitively establish causation due to potential confounding variables.<\/li>\n<\/ul>\n<\/li>\n
                                                                                            • Temporal Order:<\/strong>\n
                                                                                                \n
                                                                                              • Causation requires the cause to precede the effect in time, while correlation does not have this temporal requirement.<\/li>\n<\/ul>\n<\/li>\n
                                                                                              • Magnitude of Association:<\/strong>\n
                                                                                                  \n
                                                                                                • Strong correlation does not necessarily indicate strong causation; other factors must be considered.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                • Spurious Correlation:<\/strong>\n
                                                                                                    \n
                                                                                                  • Spurious correlations are false relationships caused by coincidental or unrelated factors.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                  • Common-causal Variables:<\/strong>\n
                                                                                                      \n
                                                                                                    • Causation might be due to a common cause affecting both variables, creating a correlation.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                    • Randomized Controlled Trials:<\/strong>\n
                                                                                                        \n
                                                                                                      • Randomly assigning treatments in experiments helps establish causation by minimizing confounding variables.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                      • Strength of Evidence:<\/strong>\n
                                                                                                          \n
                                                                                                        • Causation requires rigorous evidence beyond just a strong correlation.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                        • Scientific Theory and Mechanism:<\/strong>\n
                                                                                                            \n
                                                                                                          • A well-defined theoretical framework explaining how one variable influence another can provide stronger support for causation.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n

                                                                                                            Ethical Considerations in AP Statistics:<\/strong><\/p>\n

                                                                                                             <\/p>\n

                                                                                                              \n
                                                                                                            1. Informed Consent:<\/strong>\n
                                                                                                                \n
                                                                                                              • Respecting participants' autonomy by providing clear information about the study's purpose and potential risks.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                              • Privacy and Confidentiality:<\/strong>\n
                                                                                                                  \n
                                                                                                                • Safeguarding participants' personal information and ensuring anonymity to maintain privacy.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                • Voluntary Participation:<\/strong>\n
                                                                                                                    \n
                                                                                                                  • Participants should not be coerced or pressured into taking part in a study.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                  • Beneficence:<\/strong>\n
                                                                                                                      \n
                                                                                                                    • Ensuring that the study benefits participants or contributes to scientific knowledge.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                    • Minimizing Harm:<\/strong>\n
                                                                                                                        \n
                                                                                                                      • Taking steps to minimize any potential physical, psychological, or emotional harm to participants.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                      • Debriefing:<\/strong>\n
                                                                                                                          \n
                                                                                                                        • Informing participants about the study's true nature and purpose after data collection, especially in studies involving deception.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                        • Respect for Vulnerable Populations:<\/strong>\n
                                                                                                                            \n
                                                                                                                          • Special consideration for individuals who may be at increased risk or unable to provide informed consent.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                          • Avoiding Bias:<\/strong>\n
                                                                                                                              \n
                                                                                                                            • Ensuring that the study design and implementation are free from bias that could affect participants or results.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                            • Data Handling and Security:<\/strong>\n
                                                                                                                                \n
                                                                                                                              • Protecting collected data from unauthorized access and ensuring secure storage.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                              • Fair Representation:<\/strong>\n
                                                                                                                                  \n
                                                                                                                                • Striving for fair representation of diverse groups in studies to avoid bias in results.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                • Transparency and Honesty:<\/strong>\n
                                                                                                                                    \n
                                                                                                                                  • Clearly and honestly communicating study methods, results, and limitations to participants and the public.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                  • Long-Term Impact:<\/strong>\n
                                                                                                                                      \n
                                                                                                                                    • Considering the potential long-term consequences of the study on participants and society.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                    • Respecting Cultural Norms:<\/strong>\n
                                                                                                                                        \n
                                                                                                                                      • Being sensitive to cultural norms and practices when designing and conducting studies.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                      • Conflict of Interest:<\/strong>\n
                                                                                                                                          \n
                                                                                                                                        • Disclosing any potential conflicts of interest that could influence the study's objectivity.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                        • Peer Review:<\/strong>\n
                                                                                                                                            \n
                                                                                                                                          • Submitting research for peer review to ensure ethical standards are met and research is sound.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n

                                                                                                                                            Example: <\/strong>Investigating Bias in Survey Sampling<\/p>\n

                                                                                                                                            Scenario:<\/strong> A student is conducting a study to estimate the average amount of time high school students spend on social media each day. The student wants to ensure the survey design minimizes bias.<\/p>\n

                                                                                                                                              \n
                                                                                                                                            1. Potential Source of Bias: Self-Selection Bias\n
                                                                                                                                                \n
                                                                                                                                              • Issue: Students who choose to participate in the survey might have different social media usage patterns than those who do not participate.<\/li>\n
                                                                                                                                              • Solution: Implement a random sampling method to select participants. Assign each student in the school a unique number and use a random number generator to select a sample of participants. This helps ensure that all students have an equal chance of being selected, reducing self-selection bias.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                              • Potential Source of Bias: Non-Response Bias\n
                                                                                                                                                  \n
                                                                                                                                                • Issue: Only a portion of selected students might respond to the survey, and their responses might differ from those who do not respond.<\/li>\n
                                                                                                                                                • Solution: Follow up with non-respondents and encourage their participation. Alternatively, use techniques such as weighting to adjust for potential differences between respondents and non-respondents.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                                • Potential Source of Bias: Volunteer Bias\n
                                                                                                                                                    \n
                                                                                                                                                  • Issue: Students who are willing to participate might have different social media habits than those who are not willing to participate.<\/li>\n
                                                                                                                                                  • Solution: Implement random sampling as mentioned earlier to reduce the likelihood of volunteer bias. Also, consider offering incentives to encourage participation without revealing the study's topic.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                                  • Potential Source of Bias: Under coverage Bias\n
                                                                                                                                                      \n
                                                                                                                                                    • Issue: The survey is conducted only within one school, which might not represent the broader population of high school students.<\/li>\n
                                                                                                                                                    • Solution: Randomly select a diverse set of schools to participate in the study, and then randomly sample students from each selected school. This helps ensure a more representative sample of high school students.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                                    • Potential Source of Bias: Response Bias\n
                                                                                                                                                        \n
                                                                                                                                                      • Issue: Students might underreport or overreport their social media usage due to social desirability bias or other reasons.<\/li>\n
                                                                                                                                                      • Solution: Use anonymous surveys to encourage honest responses. Additionally, consider using techniques such as randomized response methods to mitigate response bias.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                                      • Potential Source of Bias: Interviewer Bias\n
                                                                                                                                                          \n
                                                                                                                                                        • Issue: If the survey is administered in person by interviewers, their behavior, tone, or appearance might influence respondents' answers.<\/li>\n
                                                                                                                                                        • Solution: Provide interviewers with standardized training to ensure consistent administration of the survey. Consider using technology (e.g., online surveys) to minimize interviewer bias.<\/li>\n<\/ul>\n<\/li>\n
                                                                                                                                                        • Potential Source of Bias: Sampling Frame Bias\n
                                                                                                                                                            \n
                                                                                                                                                          • Issue: The list of students from which the sample is selected might not be accurate or up-to-date.<\/li>\n
                                                                                                                                                          • Solution: Verify and update the sampling frame before conducting the survey to ensure all eligible students are included.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n

                                                                                                                                                            Key Points<\/strong><\/p>\n

                                                                                                                                                            Planning a Study<\/p>\n

                                                                                                                                                              \n
                                                                                                                                                            1. Research Objective: Clearly define the research question or objective that you want to address in your study.<\/li>\n
                                                                                                                                                            2. Population: Identify the entire group or population you wish to study, ensuring it is well-defined and relevant to your research.<\/li>\n
                                                                                                                                                            3. Sample: Determine a representative subset of the population, known as the sample, from which you will collect data.<\/li>\n
                                                                                                                                                            4. Variables: Identify the variables of interest—those that you want to measure or analyze in your study.<\/li>\n
                                                                                                                                                            5. Data Collection Method: Choose appropriate methods to collect data, such as surveys, experiments, observations, or existing records.<\/li>\n
                                                                                                                                                            6. Bias Considerations: Be aware of potential sources of bias that could affect your study's results and take steps to minimize or account for them.<\/li>\n
                                                                                                                                                            7. Ethical Considerations: Ensure that your study adheres to ethical guidelines, respects participant privacy, and obtains necessary approvals.<\/li>\n<\/ol>\n

                                                                                                                                                               <\/p>\n

                                                                                                                                                              Sampling Methods:<\/p>\n

                                                                                                                                                               <\/p>\n

                                                                                                                                                                \n
                                                                                                                                                              1. Simple Random Sampling: Every member of the population has an equal chance of being selected for the sample.<\/li>\n<\/ol>\n

                                                                                                                                                                 <\/p>\n

                                                                                                                                                                  \n
                                                                                                                                                                1. Stratified Sampling: Divide the population into distinct subgroups (strata) and then randomly sample from each stratum.<\/li>\n<\/ol>\n

                                                                                                                                                                   <\/p>\n

                                                                                                                                                                    \n
                                                                                                                                                                  1. Systematic Sampling: Select every nth element from the population to create the sample.<\/li>\n<\/ol>\n

                                                                                                                                                                     <\/p>\n

                                                                                                                                                                      \n
                                                                                                                                                                    1. Cluster Sampling: Divide the population into clusters, randomly select some clusters, and then sample all elements within the selected clusters.<\/li>\n<\/ol>\n

                                                                                                                                                                       <\/p>\n

                                                                                                                                                                        \n
                                                                                                                                                                      1. Convenience Sampling: Choose participants who are readily available or easy to reach, often leading to non-representative samples.<\/li>\n<\/ol>\n

                                                                                                                                                                         <\/p>\n

                                                                                                                                                                          \n
                                                                                                                                                                        1. Voluntary Response Sampling: Individuals self-select to be part of the sample, introducing potential bias.<\/li>\n<\/ol>\n

                                                                                                                                                                           <\/p>\n

                                                                                                                                                                            \n
                                                                                                                                                                          1. Judgmental (Purposive) Sampling: Select specific individuals or elements based on the researcher's judgment, which may introduce subjectivity.<\/li>\n<\/ol>\n

                                                                                                                                                                             <\/p>\n

                                                                                                                                                                              \n
                                                                                                                                                                            1. Randomization: Use randomization techniques, such as random assignment or random selection, to minimize bias and enhance the validity of your study.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

                                                                                                                                                                              Unit: Collecting Data Chapter: Sources of Bias & Designing Experiment. Reference: – Types of Bias & their sampling methods, non-responsive bias, Minimization, Under coverage bias, Voluntary response bias, Response bias, Experimental design, Controlled Experiments, Causation vs Correlation, Sampling techniques & Validity, Ethical considerations, Bias in surveys & Experiments After studying this chapter, you should be […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[630],"tags":[],"class_list":["post-9437","post","type-post","status-publish","format-standard","hentry","category-ap-statistics"],"_links":{"self":[{"href":"https:\/\/kapdec.com\/help\/wp-json\/wp\/v2\/posts\/9437","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/kapdec.com\/help\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kapdec.com\/help\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kapdec.com\/help\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kapdec.com\/help\/wp-json\/wp\/v2\/comments?post=9437"}],"version-history":[{"count":0,"href":"https:\/\/kapdec.com\/help\/wp-json\/wp\/v2\/posts\/9437\/revisions"}],"wp:attachment":[{"href":"https:\/\/kapdec.com\/help\/wp-json\/wp\/v2\/media?parent=9437"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kapdec.com\/help\/wp-json\/wp\/v2\/categories?post=9437"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kapdec.com\/help\/wp-json\/wp\/v2\/tags?post=9437"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}