University:
East Georgia State College
Course:
MATH 1540 | Calculus I
Academic year:

2024
Views:

388

Pages:

2
Author:

somospumasl3o

Question 1 Give a correct answer for the difference between internal and external validity is best described as: Answer The difference between internal and external validity is best described as: Internal validity tells us if the inferences drawn from the population being studied apply to the target population. External validity tells us if the inferences from the population being studied apply to other populations. Since internal validity tells about the condition which is always true for a particular sample and cannot be considered as a whole group and external validity tells us the result which is true for all cases, i.e., universal (population as a whole). Question 2 Give a correct answer for this question: In order to compare the relationship between two categorical variables, the best inference technique is? Answer The full and correct answer: Chi-Square Test for Independence is used for comparing the relationship between two categorical variables. Question 3 Explain and give a full and correct answer on how confusing the two (population and a sample) can lead to incorrect statistical inferences. Answer In statistics, statistical inference is the process of drawing conclusions from data that is subject to random variation, for example, observational errors or sampling variation. More substantially, the terms statistical inference, statistical induction, and inferential statistics are used to describe systems of procedures that can be used to draw conclusions from datasets arising from systems affected by random variation, such as observational errors, random sampling, or random experimentation. The initial requirements of such a system of procedures for inference and induction are that the system should produce reasonable answers when applied to well-defined situations and that it should be general enough to be applied across a range of situations. The outcome of statistical inference may be an answer to the question "what should be done next?", where this might be a decision about making further experiments or surveys, or about drawing a conclusion before implementing some organizational or governmental policy. In simple terms, descriptive statistics can be thought of as being just a straightforward presentation of facts, in which modeling decisions made by a data analyst have had minimal influence. On the other hand, statistical inference often assumes that data are derived from a random process. Confusing a population (all possible observations) with a sample (a subset of the population) can lead to errors such as generalizing results obtained from the sample inaccurately to the entire population, overestimating or underestimating variability, and making incorrect conclusions due to sampling bias.