Introduction to Inferential Statistics | Math in Science | Quiz

Which statement most accurately explains inferential statistics?

Inferential statistics are techniques used by scientists to summarize the characteristics of a dataset.

Inferential statistics are techniques used by scientists to interpret and make judgments about a dataset.

Inferential statistics give scientists a method to understand their data.

Inferential statistics give scientists a method to describe their data.
Which is the most accurate definition of the term “population” as it is used in statistics?

the number of organisms in a particular area

the subsample selected for study

the complete set of possible observations

all of the possible results of an experiment
To determine the ideal subsample size for an experiment, scientists need only consider the size of the entire population.

true

false
Suppose you have a corn harvest and the confidence interval of the sugar content is calculated to be 75 ± 4 mg/g. The range of values for the estimate is _________ mg/g.

72, 73, 74, 75, 76, 77, 78

71, 72, 73, 74, 75, 76, 77, 78, 79

73, 74, 75, 76, 77

70, 75, 80
What is statistical significance?

The likelihood that a statistic is highly important.

The likelihood that a statistic is not important.

The likelihood that a statistic did not occur by chance.

The likelihood that a statistic is relevant to the analysis.
Which is the best way to select a subsample of corn ears from your cornfield in order to draw conclusions about your entire corn harvest for the season?

Use a computer program to randomly select different days and locations throughout the field from which to collect your corn ears.

Select corn ears that were picked on the same day during the harvesting period.

Select the largest corn ears in the field because they will be the best representatives of the entire harvest.

Gather your corn ears from the same spot in the cornfield.
Random sampling is important for all of the following reasons EXCEPT

It helps scientists avoid introducing unintentional biases.

It helps scientists create a subsample representative of the larger population for making inferences.

It gives each individual member of the population the same chance of being selected for the subsample being studied.

It helps scientist choose the most perfect and/or interesting members of a population.
Assume you are comparing two harvests of corn, one where the mean sugar content is 75 ± 3 mg/g and the other with a mean sugar content of 80 ± 4 mg/g. How would you determine if difference in the sugar content of these two subsamples is statistically “significant”?

Conduct a t-test and if the result is 0.05 or less, then it is statistically significant.

Conduct a t-test and if the result is more than 0.05, then it is statistically significant.

Conduct an analysis of variance (ANOVA) and if the result is more than 0.05, it is statistically significant.

There is not enough information to determine if the difference in the sugar content of the subsamples is statistically significant.
Assume you’ve tracked the mean sugar content of corn for the last 10 harvests and you’ve also tracked the mean average rainfall during the growing periods. The two sets of data, known as variables, appear correlated to each other. Can you also say there is also causation?

Yes, since the amount of rainfall obviously affects the sugar content of the corn harvest.

Yes, since the amount of rainfall is an independent variable and the sugar content of the corn is a dependent variable.

No, since correlation does not automatically mean there is causation.

No, since there is no relationship between rainfall amounts and the sugar content of corn.
Two variables might show a correlation if they

are directly related to each other.

are both related to a third unknown variable.

appear correlated simply by chance.

All of the above