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80 Cards in this Set
- Front
- Back
What is a population? |
A group of individuals from an area |
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What is a sample? |
A smaller group from the group of the population taken out |
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Sampling error |
It could be as too small of a sample to accurately represent the full population. Not an actual actual "error" |
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Descriptive statistics |
(Summarize and describe data) Uses the data to provide descriptions of the population; either through numerical calculations, graphs, or tables |
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Inferential statistics |
(From a sample to make conclusions about a population) Makes inferences and predictions about a population based on a sample of data taken from population in question |
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Discrete observation |
When it's not as obvious. They are separate and distinct categories with no values in between. Only particular values. Ex: the # of kittens in a litter, # of threads in the sheet, # of stars given for an energy rating (you can measure continuous data) |
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Continuous observation |
An infinite number of possible values fall between any two observed values Ex: are you really 4'11" or are you more like 4'11.3" and you round it down.. ? etc. |
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Correlational research |
Defined as a relationship between two variables. Whole purpose is to figure out which variables are connected |
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Experimental research |
Involves manipulating one variable, to determine if changes in one variable cause changes in the another variable(s) |
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Participant variables |
(Irrelevant or unrelated to the subject being dealt with variables) related to individual characteristics of each participant Ex: background differences, mood, anxiety, intelligence |
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Environmental variables |
Factors that exist in an individual's physical environment |
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Reliability |
Basically dependable or with consistency |
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Validity |
If it did what it was meant to do. Basically accurate or correct. |
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Independent variable |
The one you're manipulating, or altering Ex: Pen color |
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Dependent variable |
What you measure (from independent variable). |
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Levels of an experiment |
Of the Independen variable that is being changed Ex: Pen color variation |
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Confounding variable |
Both participant & environmental variables. |
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What are the keys to an experiment |
Control & manipulation |
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What are some nonequivalent groups that someone would like tp study/can study but cannot manipulate? |
Gender, age, intelligence, race, etc. Correlational research would be used for this since you cannot manipulate it, & experiments require manipulation |
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Operational definition |
The end result you are trying to get out of the experiment. Ex: Amy measures academic success with sleep and parental support; the operational definition would be the graduation rate of the students being studied |
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Nominal (scale of measurement) |
(Involves names) Names DO NOT relate to any quantity |
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Ordinal (scale of measurement) |
(Involves names) Names relate to a quantity and CAN be ordered |
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Interval (scale of measurement) |
(Involves numbers) Involves equal-sized categories with an arbitrary zero value; zero DOESN'T mean zero) Ex: On a scale from 1 to 10 |
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Ratio (scale of measurement) |
(Involves numbers) Involves equal size categories were zero DOES mean zero Ex: 0 lbs. |
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Between (subject design) |
Two or more subjects in comparison |
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Within (subject design) |
Subject(s) compared to themselves |
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Can you have validity without reliability? |
No. |
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Can you have reliability without validity? |
Yes. |
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What does "x" identify? |
The first variable (The variable being examined in general) |
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What does "y" identify? |
If there is more than one; the second variable being examined |
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What does a capital n "N" mean? |
The population |
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What does a lower case "n" mean? |
The sample |
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What does the weird, jagged "E" mean? |
Summation/the "sum of" |
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What do "x" & "f" represent on a Frequency Distribution Table? |
X= variables/subjects F= frequency of "x," or the variables/subjects |
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Frequency Distribution Table |
Table with an "x" column & "f" column used to organize data |
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Grouped Frequency Distribution Table |
Table with an "x" column & "f" column used to organize data, BUT the variables in "x" are grouped (ex: 9-11) since there are so many/there is great gaps in between numbers |
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Approximately how many intervals are best for a Group Distribution Table? |
10 |
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How shpuld the interval size on Group Distribution Tables be? |
It should be easy to deal with; as 2, 5, 10 |
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What are intervals on a Frequency Distribution Table (especially Group Frequency Distribution Table)? |
The numbers of "x," or the variables, grouped Ex: 6-8 |
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How should we count the intervals on a Grouped Frequency Distribution Table to reach appropriate interval size if the numbers are higher? |
Ex: Let's say there are 5 intervals and we are counting from 50 to 54: We count "50, 51, 52, 53, 54" |
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What is an example of grouped numbers on a Grouped Frequency Distribution Table? |
45-49 50-54 55-59 (No over lap) -(& bottom score of each interval should be a multiple of the width, say your interval # is 5, then the bottom scores should be like 15, 20, 25) |
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What do these Frequency Distribution Table rules of 10 interval lines & interval size of (2, 5, 10) best match to? |
Grouped Frequency Distribution Tables |
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How do you determine whether an individual Frequency Distribution Table or a Grouped Frequency Distribution Table is best to organize the data? |
Not by how many subjects you have, BUT the value amounts between the subjects determine which you use |
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How do you determine interval size between numbers on a Grouped Frequency Distribution Table? |
By: +Subtracting the largest number, by the smallest number +Then, dividing the result of that by 10 (10 bc remember that is the number of approximate best interval rows) +Then, determining which the answer from these above steps is closest to (1, 2, 5, or 10) After, with this, we figure out our ideal interval size for the data |
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What are the two options for numerical data that are interval or ratio scales? |
Histogram & Polygon |
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What is a difference between polygon and histogram? |
A Polygon is more technical (As 5.5 on data), & a Histogram is more whole numbers. Polygon is continuous data & helps predict other values, whereas Histogram is discrete data |
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What is the diagonal line on a scatterplot/line graph? |
The diagonal line represents the trend; & the dots are the information the researchers gathered from their variables |
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Symmetrical Distribution (On graph) |
Goes from left down, to up center, & back down towards right (Pg. 17) |
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Positively Skewed Distribution (On graph) |
Starts high-ish from left, & goes down, down, down towards right (Pg. 17) |
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Negatively Skewed Distribution (On graph) |
Starts high-ish from right & goes down, down, down towards left (Pg. 17) |
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Tail (For distributions, on graph) |
The ending lower part of the graph Ex: right side of a positively skewed distribution graph, & left side of a negatively skewed distribution graph |
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Ceiling effect (On graph distributions) |
When the scores cannot go any higher due to some constraint |
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Floor effect (On graph distributions) |
When the scores cannot go any lower due to some constraint |
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Which effect does a positively skewed distribution graph go with? |
The Floor Effect |
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Which effect does a negatively skewed distribution graph go with? |
The Ceiling Effect |
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Which skewed distribution graph goes with the floor effect? |
the Positively Skewed Distributions |
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Which skewed distribution graph goes with the ceiling effect? |
Negatively Skewed Distributions |
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Which scale of measurement goes best with a bar graph? |
Nominal variables |
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What are scatter plots & line graphs best for? |
Data with 2 subjects of the variables collected (& a lot of #s of info) Ex: Height & Weight (scatter plot) vs. Exam scores (bar graph) |
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What is the difference between Histograms & Bar Graphs? |
Histograms are connected & nothing but numbers. Bar Graphs are not connected, bc each nominal subject is an individual, & since is nominal, is names on bottom & numbers to match on side |
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Mean |
Add up all numbers of data, & divide by how many numbers data there are |
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Median |
(ORGANIZE #s by numerical order) It is the middle number. If two middle numbers, add the two middle numbers & divide by 2 (bc there are two numbers) |
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Mode |
The number that appears the most |
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Parameter |
Population |
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Statistic |
Sample |
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What happens if there is more data on one side? |
The Mean is affected more |
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What happens the farther the numbers are on one side? |
The Mean is affected more |
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Would changing one score change the Mean? |
Always, yes. |
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What would to the Mean if you add a new score |
It sometimes changes. Although, if you have the same number as a Mean, it wouldn't change |
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What do you do if you have two Modes that are slightly unequal, but they both tell you important information about data? |
You list one of the major Mode and the other has the minor Mode |
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What is an outlier? |
Extreme scores that are either way lower or a way higher than the other scores in the sample (Most of the time, an error in data. Someone fell asleep, meets un-usual circumstances, etc.) Ex: 15, 17, 13, 11, 19, 13, 17, 45 Outlier= 45 |
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What would happen to data without the outlier? |
It would get lower (for the Mean) |
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Which is the best to use from Mean, Median, Mode? |
Mean (Use as the default calculation) |
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When do we use Medians? |
When the outlier is unavoidable |
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When do we use Modes? |
For Nominal data |
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What is variability? |
A numerical way of describing the spread to a distribution (How spread out your data is) |
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What are the 3 ways to measure variability? |
Range, Variance, & Standard Deviation |
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How do you find the spread of data with Range? |
By subtracting the top score, from the bottom score |
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How do you find the spread of the data with variance? |
+Write down the "x" variable column on the Frequency Distribution Table +Then, subtract the "x" variables by the Mean of the "x" data itself (X-M) +Then, square the answers (of the data subtracted from the Mean of itself) (X-M)^2 +Lastly, take the Mean of the remaining answers of data (of the data subtracted from the Mean of itself, & squared) (X-M)^2 <--M +And that's the variance. That is how you measure the spread of variability with variance |
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How do you find the spread of the data with standard deviation? |
+Find variance
[+Write down the "x" variable column on the Frequency Distribution Table +Then, subtract the "x" variables by the Mean of the "x" data itself (X-M) +Then, square the answers (of the data subtracted from the Mean of itself) (X-M)^2 +Lastly, take the Mean of the remaining answers of data(of the data subtracted from the Mean of itself, & squared) (X-M)^2 <--M]
+Lastly, just Square Root the answer to the variance. And, that is how you find the spread of variability with standard deviation. |