Introduction to statistical thinking

What is the purpose of using statistics in research?

• To make the research more subjective

• To make the research more qualitative

• To make the studies quantitative and reproducible

• To make the data analysis complex

Answer explanation

The purpose of using statistics in research is to make the studies quantitative and reproducible, allowing for objective analysis and comparison of data.

What is the difference between population and sample?

• Population and sample are not related to statistics

• Population and sample are the same

• Population is the total universe of observations, while sample is a limited number of observations extracted from the total universe

• Population is a limited number of observations, while sample is the total universe of observations

Answer explanation

The correct answer is that population is the total universe of observations, while sample is a limited number of observations extracted from the total universe.

What does the median indicate in a data set?

• The sum of all scores divided by the total number of scores

• The middle point or central score of the distribution

• The lowest score in the distribution

• The highest score in the distribution

Answer explanation

The median indicates the middle point or central score of the distribution, representing the value that separates the higher half from the lower half of the data set.

What does the range measure?

• The variability in squared units

• The number of points between the highest score and the lowest score

• The difference between each value and the mean

• The typical behavior of a data set

Answer explanation

The range measures the number of points between the highest score and the lowest score in a data set.

What is the most frequent distribution?

• Central tendency distribution

• Skewed distribution

• Normal distribution

• Non-normal distribution

Answer explanation

The most frequent distribution is the normal distribution, which is characterized by a symmetrical bell-shaped curve.

What is an independent variable in statistical variables?

• It has an effect on the dependent variable

• It is not related to the dependent variable

• It has an effect on the dependent variable, but we are not interested in it

• It is the object of study

Answer explanation

An independent variable in statistical variables has an effect on the dependent variable, which is why it is the correct choice.

What is a nominal scale used for?

• To measure order in the data

• To categorize items based on name or quality

• To measure the typical behavior of a data set

• To measure the distance between two values

Answer explanation

A nominal scale is used to categorize items based on name or quality, making it the correct choice.

What does an interval scale provide information about?

• Equal intervals and absolute zero

• Order and absolute zero

• Order, equal intervals, and absolute zero

• Order and equal intervals

Answer explanation

An interval scale provides information about order and equal intervals, which is the correct choice.

What value stands for the acceptable chances of wrongly rejecting the null hypothesis called?

• δ-value

• α-value

• β-value

• p-value

Answer explanation

The acceptable chances of wrongly rejecting the null hypothesis is called α-value, also known as the level of significance in hypothesis testing.

What are the 4 steps of hypothesis testing?

• Formulate the null hypothesis, identify the test statistics, compute the p-value, compare the p-value to an acceptable significance value

• Formulate the alternative hypothesis, compute the p-value, compare the p-value to an acceptable significance value, reject the null hypothesis

• Formulate the null hypothesis, identify the test statistics, compute the p-value, reject the null hypothesis

• Formulate the null hypothesis, compute the p-value, compare the p-value to an acceptable significance value, reject the alternative hypothesis

Answer explanation

The correct steps of hypothesis testing are to formulate the null hypothesis, identify the test statistics, compute the p-value, and compare the p-value to an acceptable significance value.