Inference and Experiments
Inference and Experiments
In understanding "Inference and Experiments" as part of the data collection process, the key areas to focus on are: randomness, sampling, experimentation, and observational studies.
Randomness
- Randomness is the foundation of statistical inference and is vital to ensure that data collected is representative of the entire population.
- The process of engaging random selection or random assignment helps to eliminate bias, ensuring the sample represents the entire population.
- Random selection involves selecting individuals to participate in a survey or study at random.
- Random assignment is employed when each individual has an equal chance of being allocated to any group in an experiment.
Sampling
- Sampling is the process of selecting a subset of individuals from the population, allowing for the collection of data.
- Simple random sampling is when each individual has an equal probability of being chosen.
- When groups, or clusters, of individuals are selected, this is known as cluster sampling.
- Stratified sampling splits the population into groups (or strata) and then selects random samples from each strata.
- Systematic sampling involves selecting every nth |individual from the population.
Experimentation
- In experimentation, researchers actively control and manipulate the variables to determine their effects.
- An experiment must include at least two conditions: the treatment condition and the control condition.
- Control group usually receives no treatment or a placebo treatment, while the experimental group receives the treatment being tested.
- In a randomised controlled trial, participants are randomly assigned to control or treatment groups to reduce bias.
- Blinding is a process in which subjects do not know whether they are in the control or treatment group. Double-blinding extends this process to researchers.
Observational Studies
- In an observational study, researchers observe and measure variables of interest without actively controlling or manipulating them.
- These studies can be retrospective (looking at historical data) or prospective (collecting new data going forward).
- In a case-control study, two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute.
- Cohort studies identify a group of individuals to study over time.
It is crucial to remember that both randomness and bias are key components to consider while collecting data for statistical analysis. Understanding the differences between sampling techniques, and the specifics of experimentation and observational studies will give the best chance to pull accurate inferences from the data.