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Understanding the design of a statistical study is crucial in determining the nature and reliability of the findings it produces. In the context of the Intel survey described in the original exercise, the design involves surveying a specific number of consumers from various countries. No experimental manipulation was performed; instead, it was a structured way to collect data on consumer behavior regarding the sharing of personal data for monetary incentives.

There are two primary types of statistical study designs: experimental and observational. In an experiment, the researcher actively imposes treatments or manipulations on the subjects to observe the effect of a specific variable. Conversely, in an observational study, the researcher simply observes and records the behaviors or responses of the study subjects without introducing any changes or treatments.

When designing a statistical study, researchers must consider aspects such as sample size, selection of participants, and methods for collecting data to ensure that the results are representative and can be generalized to a broader population. Additionally, they'll need to establish clear objectives and determine whether the study will be cross-sectional (a snapshot at one point in time) or longitudinal (over a period). The Intel survey was cross-sectional and had a large and diverse sample size, which can help in enhancing the validity of its conclusions.

Data collection methodology refers to the strategies and tools employed to gather information for a study. The method selected directly influences the accuracy and quality of the data. In the Intel survey, the methodology involved interviewing a large number of consumers across different countries to ensure a diverse and representative dataset.

Key methods include surveys, like the one used by Intel, where structured questionnaires collect quantitative data. Interviews, on the other hand, might provide more in-depth qualitative data. Observation can also serve as a data collection method, where behaviors or occurrences are systematically recorded without interference. Lastly, existing data sources such as databases or archives can be analyzed for secondary data collection.

Each method has its own set of advantages and challenges. Surveys, for example, can reach a wide audience quickly, but may suffer from biases such as non-response or self-reporting bias. Therefore, when choosing a data collection methodology, it's essential to align the method with the study’s goals and be aware of any limitations that could impact the study's outcomes.

Observational studies have distinct characteristics that set them apart from experimental designs. In the Intel survey example, there was no intervention or treatment applied; consumers were simply asked about their behaviors. This is a key trait of observational studies—they aim to understand phenomena as they naturally occur.

Characteristics of observational studies include:

• Non-manipulative approach: Researchers record what is naturally happening in the environment without altering any variables.
• Descriptive or analytical purpose: They often aim to describe characteristics of a population or analyze relationships between variables.
• Prone to external variables influence: Since there's no control or isolation of variables, observational studies can be more susceptible to confounding factors that may skew results.
• Potential for bias: Observational studies can be subject to different types of biases, such as selection bias or observer bias.

Despite these potential limitations, observational studies are invaluable in situations where experiments are unethical or infeasible, and they are fundamental in fields like epidemiology and social sciences where behaviors and trends need to be examined without interference.