Uncovering Biases

<p>Both A and B</p> Signup and view all the answers

<p>A distortion in the relationship between observed data and the underlying population</p> Signup and view all the answers

<p>When the sample used for analysis is not representative of the entire population</p> Signup and view all the answers

<p>When there are errors or inaccuracies in the measurement instruments or techniques used to collect data</p> Signup and view all the answers

<p>Oversampling</p> Signup and view all the answers

<p>To capture the characteristics and variations within each subgroup</p> Signup and view all the answers

<p>Correlation refers to a statistical relationship between variables, while causation refers to a cause-and-effect relationship.</p> Signup and view all the answers

<p>A factor or condition that influences the probability of the effect occurring.</p> Signup and view all the answers

<p>Difference-in-Differences (DID) analysis</p> Signup and view all the answers

<p>To identify the true drivers of success or failure</p> Signup and view all the answers

<p>Variables that are correlated with the independent and dependent variables</p> Signup and view all the answers

<p>Situations where the independent variable is influenced by the outcome variable</p> Signup and view all the answers

<p>Convenience Sampling</p> Signup and view all the answers

<p>All of the above</p> Signup and view all the answers

<p>The bias that occurs when conclusions or decisions are drawn based only on the individuals or entities that 'survived' a particular process or selection</p> Signup and view all the answers

<p>The tendency to seek, interpret, and favor information that confirms pre-existing beliefs or hypotheses</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>Inferential bias refers to systematic errors or deviations that can occur during the process of drawing inferences or making conclusions from data. It is important in data analysis because it can lead to incorrect conclusions, misinterpreted results, and flawed decision-making.</p> Signup and view all the answers

<p>The types of inferential bias discussed in the text are selection bias, confounding bias, and measurement bias.</p> Signup and view all the answers

<p>In a study examining the effectiveness of a new medication, participants are recruited solely from a specific hospital. This introduces selection bias because the sample used for analysis is not representative of the entire population.</p> Signup and view all the answers

<p>Selection bias occurs when the process of selecting study participants introduces systematic differences between the groups being compared, leading to biased causal inferences.</p> Signup and view all the answers

<p>Stratified sampling involves dividing the population into homogeneous subgroups (strata) and then selecting a sample from each stratum. It helps ensure representation from each subgroup and reduces bias.</p> Signup and view all the answers

<p>Non-response bias occurs when individuals selected for a study or survey do not respond, leading to a potential bias in the data analysis. It can result in underrepresentation of certain groups and distorted findings.</p> Signup and view all the answers

<p>Confirmation bias refers to the tendency to seek, interpret, and favor information that confirms pre-existing beliefs or hypotheses, while ignoring or discounting contradictory information. It can affect data interpretation and decision-making, potentially leading to biased conclusions.</p> Signup and view all the answers

<p>Random sampling is used in data analysis to reduce bias and ensure that every individual in a population has an equal chance of being chosen for the sample.</p> Signup and view all the answers

<p>Correlation refers to a statistical relationship between two variables, where changes in one variable coincide with changes in the other. Causation, on the other hand, refers to a cause-and-effect relationship between variables, where changes in one variable directly influence changes in another.</p> Signup and view all the answers

<p>Stratified sampling is used to ensure that each subgroup within a population is proportionally represented in the sample, reducing potential errors in generalizing the findings and providing a more accurate representation of the population.</p> Signup and view all the answers

<p>Cross-validation is a technique used in machine learning and predictive modeling to assess the performance of a model on different subsets of data. By evaluating the model's performance on multiple subsets, cross-validation helps reduce bias and evaluate the generalizability and predictive accuracy of the model.</p> Signup and view all the answers

<p>The role of causality in business decision-making is to provide a more accurate understanding of the relationships between variables, allowing for informed decision-making and optimizing outcomes.</p> Signup and view all the answers

<p>Experimental studies involve the use of controlled experiments to establish causality between variables. Researchers manipulate an independent variable (the cause) and observe its impact on a dependent variable (the effect), while maintaining all other factors constant.</p> Signup and view all the answers

<p>Counterfactual analysis involves comparing what actually happened (observed sample) with what would have happened under different conditions (counterfactual scenario). By comparing the observed outcome with the counterfactual, researchers estimate the causal effect of the variable of interest.</p> Signup and view all the answers

<p>Confounding variables are factors that are correlated with both the independent and dependent variables, leading to spurious causality. Researchers address this challenge by employing techniques such as randomization, matching, or statistical adjustment to control for confounding variables and isolate the true causal effect.</p> Signup and view all the answers

<p>Endogeneity refers to the presence of a relationship between the independent and dependent variables that is not purely causal. It challenges the establishment of causal relationships because it becomes difficult to determine if the independent variable leads to the outcome variable or if there is a reverse causality or common cause relationship. This can introduce bias and undermine the validity of causal inferences.</p> Signup and view all the answers

<p>Reverse causality occurs when the assumed cause is actually the effect. This challenges the establishment of causal relationships because the directionality is flipped, making it difficult to determine which variable is truly driving the relationship. For example, if we find a positive correlation between crime rates and police presence, it is difficult to determine if higher police presence reduces crime or if higher crime rates lead to increased police presence. Reverse causality undermines the ability to make accurate causal inferences.</p> Signup and view all the answers

<p>Selection bias occurs when the process of selecting study participants introduces systematic differences between the groups being compared, leading to biased causal inferences. It can arise due to self-selection (participants choosing to be in a particular group), non-random allocation to treatment or control groups, or loss to follow-up. Selection bias undermines the validity of causal inferences because the groups being compared are not truly representative of the population, introducing potential confounding factors and biasing the results.</p> Signup and view all the answers

<p>Researchers can use various techniques to address selection bias, including random assignment, matching, propensity score analysis, and sensitivity analysis. Random assignment involves randomly assigning participants to treatment or control groups to ensure equal representation of different characteristics. Matching involves pairing participants in treatment and control groups who have similar characteristics to minimize differences. Propensity score analysis involves estimating the probability of assignment to treatment or control groups based on observed characteristics and adjusting for these probabilities in the analysis. Sensitivity analysis involves assessing the robustness of results to different assumptions about selection processes. These techniques help mitigate selection bias and improve the validity of causal inferences.</p> Signup and view all the answers