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Explain the concept of Statistical Inference.
Using data from a sample to draw conclusions about a larger population.
Explain the concept of Sampling Variability.
If you take multiple random samples from the same population, each sample will likely give you slightly different results.
Explain Random Assignment and its importance in experiments.
Randomly assigning experimental units to treatment groups. It helps ensure observed differences are due to the treatment, not other factors, allowing for causal inferences.
Explain the importance of Sample Size in statistical studies.
Larger samples tend to give more accurate estimates of the population parameter because they are more representative and less affected by random variation.
Explain the concept of Generalizability in experiments.
The extent to which the results of an experiment can be applied to a larger group, dependent on how representative the experimental units are of that group.
Explain the concept of Statistical Significance in hypothesis testing.
It helps us determine if our results are real or just due to random variation. It's the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct.
What are the differences between a Parameter and a Statistic?
Parameter: Describes a population, fixed value | Statistic: Describes a sample, varies with samples
What are the differences between Random Selection and Random Assignment?
Random Selection: Used to select participants from a population, allows for generalization | Random Assignment: Used to assign participants to treatment groups, allows for causal inferences
What are the differences between causation and correlation?
Causation: One variable directly influences another. | Correlation: Variables are related, but one does not necessarily cause the other.
What are the differences between the treatment group and the control group?
Treatment Group: Receives the treatment being tested. | Control Group: Does not receive the treatment, often receives a placebo.
What are the differences between random sampling and stratified sampling?
Random Sampling: Every member of the population has an equal chance of being selected. | Stratified Sampling: The population is divided into subgroups (strata), and random samples are taken from each stratum.
What is the definition of Population?
The entire group you're interested in studying.
What is the definition of Sample?
A smaller, manageable subset of the population that you actually study.
What is the definition of Parameter?
A numerical value that describes a population (e.g., the true average height of all women).
What is the definition of Statistic?
A numerical value that describes a sample (e.g., the average height of women in your sample).
What is Random Assignment?
Randomly assigning experimental units to different treatment groups to balance pre-existing differences.
What is Statistical Significance?
A difference between groups so large that it's unlikely to have occurred by random chance alone.