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Topic 43. Descriptive and inferential statistics.
Question 1. Which branch of statistics (“inferential” or “descriptive”) helps researchers to summarize data so they can be easily comprehended?
It is descriptive branch of statistics which is destined to summarize and organize data so that they are easily comprehended. Inferential statistics is used to draw inferences basing on the samples of the population, and make generalizations. For summarizing descriptive statistics is used.
Question 6. Is margin of error a “descriptive” or an “inferential” statistic?
Margin of error is a frequently used common term aimed to denote inferential statistics; margin or error indicates the interval where the studied value appears with a certain probability, and this describes results obtained from a sample (e.g. inferential).
Topic 44. Introduction to the Null Hypothesis.
Question 4. The null hypothesis says the true difference equals what numerical value?
Version C on null hypothesis states that true difference between the two groups equals zero. This also might be expressed as the fact that there are no true differences between the two groups, and that in fact, the considered difference was obtained by sampling error.
Question 6. The expression p<.05 stands for what words?
The statement that probability < .05 (p<.05) means that the significant test has shown the following fact: probability that null hypothesis is correct is less than 5 cases out of 100.

Topic 45. Scales of measurement.
Question 4. Which scale of measurement has an absolute zero?
Among scales of measurement, only the highest level scale – ratio scale – has an absolute zero.
Question 6. Objective, multiple-choice achievement tests are usually assumed to measure at what level?
Objective multiple-choice achievement tests measure relative values, i.e. the values that do not have some absolute scale of measurement. Thus, such type of tests measure at the interval scale level.
Topic 46. Descriptions of nominal data.
Question 6. Are “percentages” or “proportions” easier for most individuals to comprehend?
Percentage indicates the amount of studied phenomenon presence among every 100 trials; proportion is in fact 100 times less than percentage, and indicates what part of one trial related to the studied phenomenon. Although both figures, in fact, describe the same value, for most individuals it is easier to understand percentage rather than proportions since percentages are closer to real life.
Topic 47. Introduction to the Chi-Square Test
Question 7. Does “p<.05” or “p>.05” usually lead a researcher to declare a difference statistically significant?
“p<.05” indicates that null hypothesis may be rejected as its probability is quite low. Analogously, when “p>.05” holds, this means that null hypothesis should not be rejected because its possibility is not low enough to eliminate null hypothesis from the list of possible error sources. It can be stated that there exists a statistically significant difference between two groups when a) there are no bias in the procedure and b) null hypothesis is rejected. Thus, “p<.05” should hold for statistically significant difference.
Question 8. If a researcher fails to reject a null hypothesis, is the difference in question statistically significant?
When null hypothesis cannot be rejected, it remains in the list of possible sources of errors. The situation when null hypothesis should not be rejected is called statistically insignificant result. Thus, the difference in question is statistically insignificant. However, this does not show that the suggested difference does not exist; this fact only shows that there are random sampling errors in the experiment.
Topic 48. A Closer Look at the Chi-Square Test
Question 5. What is the name of the error researchers make when they fail to reject the null hypothesis when in fact it is incorrect hypothesis?
There are two types of errors: type I (null hypothesis is rejected though it is correct) and type II (null hypothesis not rejected though it is incorrect). When the researchers fail to reject the null hypothesis when in fact it is incorrect hypothesis – it is Type II error.
Question 6. What is the name of the error researchers make when they reject the null hypothesis when in fact it is a correct hypothesis?
There are two types of errors: type I (null hypothesis is rejected though it is correct) and type II (null hypothesis not rejected though it is incorrect). When the researchers reject the null hypothesis when in fact it is a correct hypothesis – it is type I error.