Reading...
Play button
Play button
Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
17 Cards in this Set
- Front
- Back
|
The typical population of interest to the behavioral science researcher is extremely large; what serioius problem does this create? How does the use of samples help to solve this problem?
|
Because it is impossible to record data of the whole population. By using samples we can represent the population with taking a less data that is more managable.
|
|
|
What new difficulty is created by the use of samples? What type of statistics helps to solve this problem?
|
To make sure the accuracy of the data is correct. Using inferential statistics are techniques that help compare against the general population.
|
|
|
What are three major varieties of inferential statistics?
|
Point estimate which a sample presdicts a parameter.An interval estimate is the possibility of a range of scores. To assess the probabilty of obtaining certain kinds of sample results under certain population conditions.
|
|
|
What is an experimental sampling distribution? Why is this type of statistical model not used in inferential statistics?
|
The distribution is what is distributed as, it is not used because it does not accurately describe population and we don't always have all the information.
|
|
|
What is a theoretical sampling distribution? When is it reasonable to use the normal curve as the theoretical distribution (i.e., statistical model)?
|
The distirbution is a perfectly normal. When doing sample statistics.
|
|
|
We wish to draw infrences about the mean of one population, based on a statistic whose sampling distribution is normal-namely, the sample mean. Why should teh standard deviation of the raw scores not be used as the measure of variability? How does the variability of a distribution of sample means differ from that of raw scores?
|
Because as the more scores away from the mean the greater the standard deviation value, more scores higher standard deviation. It is the ST DEV divided by the sq root of teh sample size.
|
|
|
What is the standard error of the mean? Why can't it be measured directly? How is it estimated?
|
STD DEV/Sq Root of Sample Size. Bc there infinately amount of possibilities to add up.
|
|
|
How is the z score calculated when dealing with sample means instead of individuals?
|
The same way except a mean is used instead of individual score and standard error instead of std dev.
|
|
|
What is a p value?
|
The probability that the two populations are different
|
|
|
What two hypotheses are made prior to conducting the statistical analysis?
|
Null and Alternative Hypothesises
|
|
|
Which of these hypotheses is assumed to be true?
|
Null
|
|
|
What is a Type I error?
|
rejecting the null hypothesis where the null hypothesis is actually true.
|
|
|
What is the criterion of significance, and how is it related to a Type 1 error?
|
Higher level of criterion less likely a type I error will be made.
|
|
|
What is the probability of a Type 1 error?
|
Is equal to alpha
|
|
|
How does the one-tailed p value differ from the two-tailed p value?
|
the probability is cut it two
|
|
|
What are critical values? How are critical values used in null hypothesis testing?
|
The value that is determined to either reject or accept the null hypothesis.
|
|
|
What are the cricial values for z in a two-talied significance test using the .05?
|
1.96
|
