Quiz 1

Frage	Antworten
why focus on stats?	you cannot understand what physiologists do or ever do it yourself, w/o knowledge of stats.
why are stats essential for physchological science ?	psychologists goal is to provide objective answers to questions about human behavior
why should we love stats ?	because being able to answer questions objectively is a great thing especially when questions are controversial
stats allow us to______and______ data	organize and interpret
why must data be organized ?	w/o organization , data are just a meaningless bunch of numbers and can't be used to answer interesting questions
calculating averages is a_________ technique	statistical
descriptive statistics	describing the behavior of the specific people in a specific sample
interpretation	figuring out what data mean, drawing conclusions from data interpretation
psychologists care about what the data mean for people in_______	the population
psychologists want to know about ________, but are forced to study _______ because it's impossible to study every single person in a popultion	-populations -samples
using __________ _____ to look at the behavior of people in a sample does not , by itself tell us anything about people in the population	descriptive stats
inferential statistics	used to figure if a difference in a sample is good evidence that the same difference would exist for people in general
inferential statistics	let us infer something about a population based on a sample
together _________ and ________ ____ allow us to answer questions about populations by studying samples	descriptive and inferential statistics
psychological science requires ______	creativity
turning complex behavior into numerical facts requires a great deal of ______	creativity
variable	a characteristic of a person that can have at least 2 possible values
for variables the values are either _______ or ________	-numbers -categories
aa persons particular value is the persons _________	score
numeric values	variables in which values are numbers
categorical values	variables in which values are categories
its important to recognize the difference between categorical and numeric values because	the kinds of variable you have determines the statistical tools you can use with the variables
3 types of investigation where data come from	1. observational studies 2.experiements 3.quasi experiments
observational studies	-can only show that when people differ on an explanatory variable , they may also differ on a response variable * there may be a third variable that causes the difference in response variables
explanatory variable	a variable we think might partially explain behavior ( sometimes called independent variable)
response variable	a variable we want to measure ( sometimes called dependent variable)
observational studies limitation:	don't provide evidence if causation , they can't reveal whether the explanatory variable caused a difference in the response variable
observational studies are often presented in the press and elsewhere as if they provide evidence of _________	causation
experiments	type of investigation that can provide evidence of causation
experiment features:	- shared with observational studies but -involve randomly assign sample members to particular values (or diff. conditions) - no worry/ less worry about third variable problem
quasi- experiements	-sometimes random assignment of subjects to levels of the explanatory variable is difficult, if not impossible -researchers might compare 2 groups that naturally differ
reseachers in quasi experiments think they are observing a marker for a ______________ and then consider that ____________ to be the explanatory variable	third variable
in observational studies , the observed variable is considered to be the ____________________	explanatory variable
quasi experiments have all the same features as observational studies and do not have _________	random assignment
different values of an explanatory variable are sometimes called	the different levels of the variable
randomness is important in 2 steps of data collection	-selection of study participants -assignment of participants
third variable problem	the problem that can make it hard to argue that explanatory variables cause differences in response variables
why select participants randomly ?	we want samples to represent the population
random selection produces a	representative sample
why assign participants to conditions randomly ?	-avoid bias -produces groups that on average are similar to each other - it's vital that groups in an experiment are similar
dependent measure =	response variable
2 response variables	-categorical variables -numerical variables
what are inferential stats for ?	deciding if a difference that occurs in a sample is good evidence that the same difference would occur in the population
why are there so many different inferential statistical tests ?	because investigations can be set up in different ways and you need a specific test for each specific set up
different set ups are called	different experimental designs
Paired T-Test	- there is 1 and only 1 group of SS -each subject was exposed to 2 and only 2 levels of explanatory variables
Two Sample T-Test	- 2 and only 2 groups of subjects -each subject was expose to 1 and only 1 level of the explanatory variable
One Way ANOVA (analysis of variance )	-3 or more groups of subjects -each subject was exposed to 1 and only 1 level of explanatory variable
Random sampling	psychologists achieve representative samples
random assignment	psychologists create groups of subjects that are similar to each other
Chi-square Test	response variable is categoical
Factorial ANOVA ( analysis of variance)	2 (or more) explanatory variables
inferential statistical tests are tools for	interpreting data
we want to organize data to show the	distributions of a variable
variables are shown in	bar graphs= categorical histograms= numeric
bar graphs show	the different values the variable can have
histograms show	the different distributions of the numeric variable
1 peak in histograms =	unimodal
2 (or more peaks ) in histograms =	-bimodal (2) -multimodal (>2)
idealized depiction	bell curve/ bell shape
midpoint=	between lowest value and highest value in a sample
If frequencies skew to the left=	negatively skewed
if frequencies skew to the right=	positively skewed

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	Erstellt von Akilah Madry vor fast 8 Jahre