BESC STAT POST TEST CH 5 QUESTIONS AND ANSWERS | LATEST VERSION | GRADED A+
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BESC STAT POST TEST CH 5
QUESTIONS AND ANSWERS | LATEST
VERSION | GRADED A+
Why is random assignment important in experiments?
Random assignment reduces bias by ensuring each participant has an equal chance of being
assigned to any group, improving validity of results.
What does the c...
BESC STAT POST TEST CH 5
QUESTIONS AND ANSWERS | LATEST
VERSION | GRADED A+
Why is random assignment important in experiments?
✔✔Random assignment reduces bias by ensuring each participant has an equal chance of being
assigned to any group, improving validity of results.
What does the central limit theorem state?
✔✔The central limit theorem states that the distribution of sample means approaches a normal
distribution as the sample size increases, regardless of the population's shape.
What is statistical inference?
✔✔Statistical inference involves making conclusions about a population based on sample data,
often using estimates and hypothesis testing.
What is a confidence level, and what does it indicate?
✔✔A confidence level indicates the degree of certainty that a confidence interval contains the
population parameter, commonly set at 95% or 99%.
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, How does increasing the sample size affect the margin of error?
✔✔Increasing the sample size reduces the margin of error, making estimates more precise and
reliable.
What is the primary purpose of regression analysis?
✔✔Regression analysis examines relationships between variables to make predictions about a
dependent variable based on one or more independent variables.
What does it mean for results to be statistically significant?
✔✔Statistically significant results indicate that the observed effect is unlikely to be due to
chance, suggesting a real difference or relationship exists.
What is the law of large numbers?
✔✔The law of large numbers states that as sample size increases, the sample mean approaches
the population mean, improving estimate accuracy.
What is meant by variability in a dataset?
✔✔Variability refers to how spread out data points are, showing the diversity or dispersion
within a dataset.
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