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Review of Dat-N-Stat (part 2)

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Review of Dat-N-Stat (part 2)

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The second review of the data management and statistics content that will be relevant for this class.

Created by

Wendy
Wendy
Austria

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Review of Dat-N-Stat (part 2)
 

Review of Dat-N-Stat (part 2)Online version

The second review of the data management and statistics content that will be relevant for this class.

by Wendy
QR
1

Which of the following is most affected by extreme values or outliers?

2

If a dataset is skewed due to a few extreme values, which measure of central tendency gives the best representation of the “typical” value?

3

Which measure of central tendency identifies the most frequently occurring value in a dataset?

4

What does absolute frequency measure?

5

If 10 students out of 30 wear glasses, what is the relative frequency of “students with glasses”?

6

In a normal distribution, what percentage of data lies within one standard deviation of the mean?

7

What is the purpose of inferential statistics?

8

In general hypothesis testing (e.g., testing group differences or correlations), what does the null hypothesis (H₀) typically state?

9

What is a Type I error?

10

Which of the following statements about p-values is true?

Explanation

The mean includes every data point in its calculation, so one very high or low value can skew it significantly.

The median is more resistant to outliers and better represents the center of a skewed distribution.

The mode tells you which value appears most often, such as the most common shoe size or favorite color.

Absolute frequency is the raw count of occurrences of a value.

Relative frequency = 10 ÷ 30 = 0.33 or 33%.

In a normal distribution, about 68% of data falls within ±1 standard deviation of the mean.

Inferential statistics use sample data to make generalizations about a larger population.

In general hypothesis testing, the null hypothesis states that there is no effect or no relationship — this is the default assumption that we test against. However, in specific statistical tests, like RMSEA or Levene’s test, the null hypothesis may take a more tailored form.

A Type I error means you incorrectly conclude there is an effect or difference when there isn’t. It is a “false positive.”

A p-value under 0.05 typically indicates statistical significance. In other words, the observed effect is unlikely to be due to chance alone.

 
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