exam 1 Flashcards

Question

Central tendency

Answer 1

describe the central point or typical value of a distribution Goal– allows researchers to summarize/condense a large data set into a single value Allows us to quickly compare two data sets that have collected similar information Ex– exam one scores in this class versus other psy 350 class

Answer 2

how spread out the scores are around the centrail point Together, these measure describe distributions of scores Usually reported together Descriptive statistics– both central tendency and variability measures Both define the shape of distribution

Answer 3

most commonly used measure of central tendency Only works if measure is numerically coded

Answer 4

moving the distribution on a number line Adding/multiplying a constant value to every single score in sample

Answer 5

Mean: Is multiplied by the constant value. Standard Deviation: also multiplied by the constant value (spread changes). - **Distribution**: Stretches or compresses depending on whether the constant is greater than or less than 1.

Answer 6

Mean-- Increases by the constant value. Standard Deviation-- stays the same

Answer 7

It is always inappropriate to compute a mean for nominal data No numerical value assigned to categories

Answer 8

Usually inappropriate to compute mean for ordinal data If you have a lot of categories its might be appropriate 8+ categories Easier to just give percentages that fall into each category Wiggle room with ordinal data, but never with nominal

Answer 9

midpoint of a list of scores that are sorted in order from smallest to largest Median is less affected by extreme scores and skew Stays in the center of a distribution Should be used over the mean in these situations Median does a better job at representing the distribution over mean Also does a better job even if skew is present

Answer 10

perfect-- 0 -2 or lower-- problematic negative skew 2 or higher-- problematic positive skew

Answer 11

most frequently occurring category or score in a distribution Only one to report for nominal data

Answer 12

Normal distribution– means, median and mode will be equal to each other and be the same value Skewed Mode will be at the peak Mean will be toward the tail Median usually is between mean and the mode Order (from left to right)– mode, median, mean

Answer 13

Describe the spread in a distribution Important component of most inferential statistical tests

Answer 14

Range Variance Standard deviation

Answer 15

measures the average squared distance between a score and the mean

Answer 16

standard average distance between a score and the mean Most commonly reported measure of variability Both tell us how much spread is around the mean

Answer 17

mean squared distance from the mean

Answer 18

standard, mean distance from the mean

Answer 19

A sample statistic is unbiased if the average value of the statistics is systematically equal to the population parameter On average sample statistic will be that parameter after repeating it over and over again

Answer 20

A sample that is biased if the average value of the statistic is systematically different from the population parameter Statistic samples won't be equal after repeating it over and over again

Answer 21

number of scores in the sample that are free to vary from the mean Example If M=6 Frist score– 8 Second score– 3 Third score has to be 7 to result in a mean of 6 First two are free to vary, where third is fixed

Answer 22

if a constant is added to every score in the distribution, the standard deviation does not change Adding a constant to every score just shifts distribution If its multiplied by a constant it does change Increased distance = increases standard deviations

Answer 23

standard score We convert X into a z score to tell us the exact location of any given score

Answer 24

Positive– z score is higher than sample mean Negative– z score is lower than sample mean If z score is 2.5– 2.5 standard deviations above the sample mean When converting raw scores into z score– mean becomes 0, standard deviation becomes 1

Answer 25

A distribution where all the original scores have been changed so that they now have a set (or specific) mean and standard deviation

Answer 26

the form of the distributions does not change Mean now is 0 Standard deviation is now 1 Advantages– puts everything on the same scale so we can make comparisons between different distributions

Answer 27

Must pay attention to the sign (positive or negative) and absolution value (how large is it) Sing tell us whether the score is above or below the mean Negative– below Positive– above Absolute value– tells us how many standard deviations the score is from the mean Z score of 0 means it is the mean

Answer 28

We typically deal with likelihood instead of certainty

Answer 29

means that every item has an equal chance of being chosen, and the chances don't change from one pick to the next, even if you're picking more than one item

Answer 30

z score is positive Vice versa for negative Usually more interested in the tail Rarest z-score will always be the one furthest from the mean

Answer 31

ollection of sample means for all possible random samples of a given size from a population This is a specific type of sampling distribution, or a distribution of statistics obtained from all possible samples The more samples you draw the more normal and symmetrical your distributions of samples will look

Answer 32

Distribution of individual scores in the population Original set of scores in the population we want to study Example– 333 million americans Distribution of individual scores in a sample Actual set of scores from our sample we are able to study Example– 20,000 american you pulled for your sample Disruption of sample means Theoretical set of mean from all possible random samples Used to draw conclusions

Answer 33

The average of our distribution of sample mean will always be equal to the population mean Expected value of M– most likely value for the mean in any sample The standard deviation for the distribution of sample means is smaller than the population standard deviation

Answer 34

the larger our sample sizes (n) are, the more probable it is that a sample mean will be close to the population mean As our sample size gets larger, our standard error gets smaller

Answer 35

Sampling error is the diff between one sample mean and the population mean Standard error is the average sampling error across all possible samples

Answer 36

30 If you are taking samples of at least n = 30, you are guaranteed a normal distribution of sample means If it's not 30, but the population distribution is normal, you are still guaranteed a normal distribution of sample means

Answer 37

Purpose– test for a difference between the true population parameter and the observed sample statistic Decide between two explanations Difference is som small, there does not appear to be a treatment effect Difference is so big that there appears to be a treatment effect Can it reasonably be explained by sampling error?

Answer 38

this is known mean from the original untreated population you want to compare your sample to What you are drawing your sample from Have to know what the mean and sd are

Answer 39

unknown mean among the population that receives the treatment you are trying to estimate with your sample After treatment if it is different enough from sample mean it now belongs to unknown treated population Main goal– does the random sample still represent the treated population or now represent an unknown treated population

Answer 40

state that there is no difference in the population as a result of some treatment Assumptions there is no effect in the study Assumes our “treated” sample comes from the same untreated population In the example, average stress would still be 50 The null hypothesis is what we are testing so we assume thats its true and after applying treatment it will be the same Trying to find enough evidence enough the null so we can reject it Is diff large enough we can conclude null is not true?

Answer 41

states that there is a difference in the general population as a result of some treatment Assumes any change we see is beyond what we would expect because of sampling error Assuming its not plausible that our treated sample comes from the original untreated population We don't make any specific prediction about the alternative hypothesis, just that it is ot the same as the null Null– H0 = 50 Alt– H1(equal sign with a slash) 50

Answer 42

Choose between the null and alternative hypotheses based on how likely it is that we would observe our test statistic if the null hypothesis were true If it's likely, the the null is probably correct and we would prefer it Vice versa for alternative Have to decide what rare is going to be Have to make this choice in advance– before we ever look at the data

Answer 43

probability value used to define what is likely and what is unlikely If you think 15% is an unlikely score, alpha level would be .15 In psychology 5% (.05) is the typical standard 5% of all possible sample means can be classified as unlikely to occur

Answer 44

Can't do it with a skewed distribution– has to be at least 30 people in our sample We want to pick a z score with both its positive and negative tails values to create 5% (+/- 1.96)-- gives you 5% of all the possible scores

Answer 45

defined by critical z score (+/- 1.96) that encompass 5% of all possible sample means Values you would deem super unlikely to occur if null true In theory, any score in this region is almost impossible if the treatment has no real effect

Answer 46

Rescutir sample, apply treatment, compute mean score Convert the sample statistics to a test statistic, which is just a z-score in the case of a z test Assuming the treated sample belongs to unknown treated population, you want to know how probable it would be to find a z score of that magnitude Probable– accept null hypothesis Unprobale– accept alternative hypothesis

Answer 47

Use unit normal table If it is in the critical region, we conclude that the difference is significant In this case we reject the null If it's in the body, we would conclude we did not find enough evidence to reject the null In this case we fail to reject the null

Answer 48

We ever accept the alternative hypothesis because that not what we are actually testing (always testing the null), but you can support it For the null we either reject (p<.05) or fail to reject the null (p > .05)

Answer 49

false error Rejecting null when you shouldn't have A sample statistic falls in the critical region even though the treatment has no real effect in the population

Answer 50

false negative Treatment effect does exist, we just failed to detect it More likely to make a type two error Lack of statistical power Power– probability the test will reject the null when the treatment does have a real effect Depend on Size of treatment effect Size of sample– larger sample size, more power Experimental validity

Answer 51

Increase effect size Conduct an experiment instead of an observational study Use more precise measurement Increase sample size– what we have more control over Make sample more representative of the population Reduces standard error of the distribution of sample means– makes it skinnier Decrease population standard deviation Limit target population Reducing population so people are more similar “20 year old, female, right handed, college student” Increase alpha level Ex– making it .10 instead of .05