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Questions and Answers
When is inferential statistics valuable?
When is inferential statistics valuable?
- When it is not possible to examine each member of an entire population (correct)
- When the variables are not normally distributed
- When the entire population can be easily accessed
- When the sample size is small
What do descriptive statistics provide a summary of?
What do descriptive statistics provide a summary of?
- Data in the form of mean, median, and mode (correct)
- Data in the form of quartiles and percentiles
- Data in the form of correlation and regression coefficients
- Data in the form of range, variance, and standard deviation
In a negatively skewed distribution, where is the mass of the distribution concentrated?
In a negatively skewed distribution, where is the mass of the distribution concentrated?
- On the right of the figure (correct)
- On the left of the figure
- Equally on both sides of the figure
- In the center of the figure
What do most biological variables usually do around a central value?
What do most biological variables usually do around a central value?
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What does inferential statistics use a random sample of data to do?
What does inferential statistics use a random sample of data to do?
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Study Notes
Inferential Statistics
- Inferential statistics is valuable when making predictions or inferences about a population based on a sample.
- It allows researchers to draw conclusions beyond the immediate data and test hypotheses.
Descriptive Statistics
- Descriptive statistics provide a summary of data, offering insights into the central tendency, variability, and distribution of the dataset.
- Common measures include mean, median, mode, and standard deviation.
Negatively Skewed Distribution
- In a negatively skewed distribution, the mass of the distribution is concentrated on the right-hand side.
- This typically results in a longer tail on the left side, indicating the presence of lower values.
Biological Variables
- Most biological variables tend to cluster around a central value, exhibiting normal distribution in many cases.
- This clustering is influenced by natural processes and genetic factors.
Use of Random Samples in Inferential Statistics
- Inferential statistics employs a random sample of data to estimate population parameters and make predictions.
- It minimizes bias and allows for generalizations from the sample to the larger population.
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