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Chapter 1-6

# Chapter 1-6 Terminology

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University of Toronto Scarborough

Psychology

PSYB07H3

Douglas Bors

Fall

Description

PSYB07 – Definitions
Chapter 1 – Displaying the Order in a Group of Numbers Using Tables and Graphs
• Statistics – branch of mathematics that focuses on the organization, analysis, and interpretation of a group of numbers
• Descriptive statistics – procedures for summarizing a group of scores or otherwise making them more understandable
• Inferential statistics – procedures for drawing conclusions based on the scores collected in a research study but going beyond them
• Variable – characteristic that can have different values
• Values – possible number or category that a score can have
• Score – particular person\s value on a variable
• Numeric variable – variable whose values are numbers
• Equal interval variable – variable I which the numbers stand for approximately equal amounts of what is being measured
• Ratio scale – an equal-interval variable is measures on a ratio scale is it as an absolute zero, meaning that the value of zero on the
variable indicates a complete absence of the variable
• Rank order – numeric variable in which the values are ranks
• Nominal variable – variable with values that are categories
• Levels of measurement – types of underlying numerical information provided by a measure, such as equal interval, rank order, etc.
• Discrete variable – variable that has specific values and that cannot have values between these specific values
• Continuous variable – variable for which, in theory, there are an infinite number of values between any two values
• Frequency table – ordered listing number of individuals having each of the different values for a particular variable
• Interval – range of values in a grouped frequency table that are grouped together
• Grouped frequency table – frequency table in which the number of individual is given for each interval of values
• Histogram – barlike graph of a frequency distribution in which the values are plotted along the horizontal axis and the height of each bar
is the frequency of that value
• Frequency distribution – pattern of frequencies over the various values
• Unimodal distribution – frequency distribution with one value clearly having a larger frequency
• Bimodal distribution – frequency distribution with two approximately equal frequencies, clearly larger than the other values
• Multimodal distribution – frequency distribution with two or more high frequencies separated by a lower frequency
• Rectangular distribution – frequency distribution in which all values have approximitaley the same value
• Symmetrical distribution – distribution in which the pattern of frequencies on the left and right side are mirror images of each other
• Skewed distribution – distribution in which the scores pile up on one sife of the middle and are spread out on the other side
• Floor effect – situation in which many scores pile up at the low end of a distribution (creating skewness to the right) because it is not
possible to have any lower score • Ceiling effect – situation in which many scores pile up at the high end of a distribution (creating skewness to the left) because it is not
possible to have a higher score
• Normal curve – specific, mathematically, bell-shaped frequency distribution that is symmetrical and unimodal
• Kurtosis – extent to which a frequency distribution deviates from a normal curve, in terms, of whether its curve in the middle is more
peaked (having more scores in the tails of the distribution) or flat (have fewer scores in the tails of the distribution) than the normal curve
Chapter 2 – Central Tendency and Variability
• Central tendency – typical or most representative value of a group of scores
• Mean – arithmetic average of a group of scores; sum of the scores divided by the number of scores
• Mode – value with the greatest frequency in a distribution
• Median – middle score when all the scores in a distribution are arranged from lowst to highest
• Outlier- score with a n extreme value, very high or low, in relation to the other scores in the distribution
• Variance – measure of how spread out a set of scores are; average of the squared deviations from the mean
• Deviation score – score minus the mean
• Squared deviation score – square of the difference between a score and the mean
• Sum of the squared deviations – total of each score`s squared differences from the mean
• Standard deviation – square root of the average of the squared deviations from the mean; the most common descriptive statistic for
variation; approximately the average amount that scores in a distribution vary from the mean
• Computational formula – equation mathematically equivalent to the definitional formula, easier to use for figuring by hand, it does not
directly show the meaning of the procedure
• Definitional formula – equation for a statistical procedure directly showing the meaning of the procedure
Chapter 3 – Some Key Ingredients for Inferential Statistics
• Z score – number of standard deviations that a score is above or below, if negative, the mean of its distributions; it is thus an ordinary
score transformed so that it better describes the score`s location in a distribution
• Raw score – ordinary score, or any number in a distribution before it has been made into a z score or otherwise transformed
• Normal distribution – frequency distribution that follows a normal curve
• Normal curve – specific, mathematically defined, bell-shaped frequency distribution that is symmetrical and unimodal
• Normal curve table – table showing percentages of scores associated with the normal curve; the table usually includes percentages of
scores between the mean and various numbers of standard deviations above the mean and percentages of scores more positive than
various numbers of standard deviations above the mean
• Population – entire group of people which a researcher intends the results of a study to apply; larger groups to which inferences are
made on the basis or the particular set of people (sample) studied
• Sample – scores of the particular group of people studied; usually considered to be representative of the scores in some larger
population • Random selection – method for selecting a sample that uses truly random procedures, usually meaning that each person in the
population has an equal chance of being selected; one procedure is for the researcher to behind with a complete list of all the people in the
population and select a group of them to study using a table of random numbers
• Population parameter – actual value od the mean, standard deviation, and so on, for the population; usually population parameters are
not know, though often they are estimated based on the information in samples
• Sample statistics – descriptive statistics, such as the mean or standard devation, figured from the scores in a group od people studied
• Probability – expected relative frequency of an outcome; the proportion of successful outcomes to all outcomes
• Outcome – term used in discussing probability for the result of an experiment
• Expected relative frequency – number of successful outcomes divided by the number of total outcomes you would expect to get it you
repeated an experiment a large number of times
• Long run relative frequency interpretation of probability – understanding probability as the proportion of a particular outcome that
you would get if the experiment were repeated many times
• Subjective interpretation of probability – way of understanding probability as the degree of one’ certainty that a particular outcome will
occur
Chapter 4 – Introduction to Hypothesis Testing
• Hypothesis testing – procedure for deciding whether the outcome of a study, results from a sample, supports a particular theory or
practical innovation
• Hypothesis – prediction, often based on informal observation, previous research, or theory that is tested in a research study
• Theory – set of principles that attempt to explain one or more facts, relationships or events
• Research hypothesis – statement in hypothesis testing about the predicted relation between populations, often prediction of a
difference between population means
• Null hypothesis – statement about the relation between populations that is the opposite of the research hypothesis; statement that in
the population there is no difference, or a difference opposite to that predicted, between populations; contrived statement set up to examine
whether it can be rejected as part of hypothesis testing
• Comparison distribution – distribution using in hypothesis testing; it represent the population situation if the null hypothesis is true; it is
the distribution to which you compare the score based on your sample’s results
• Cutoff sample score – point in hypothesis testing, on the comparison distribution at which, if reached or exceeded by the sample score,
you reject the null hypothesis… also called critical value
• Conventional levels of significance (p < 0.05, p <0.01) – levels of significance
• Statistically significant – conclusion that the results of a study would be unlikely if in fact the sample studied represents population that
is no different from the population in genera; an outcome of hypothesis testing in which the null hypothesis is rejected
• Directional hypothesis – research hypothesis predicting a particular direction of difference between population, for example, a
prediction that the population like the sample studied has a higher mean than the population in general
• One tailed test – hypothesis testing procedure for a directional hypothesis; situation in which the region of the comparison distribution in
which the null hypothesis would be rejected is all on one side (tail) of the distri

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