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Quant Exam 1
| Term | Definition |
|---|---|
| Statistical Inference | making a decision or reaching a conclusion based on the data |
| Contingency Table | displays the frequency distribution and relationship between two or more categorical variables |
| Probabilities Must Add To... | 100% OR 1 |
| Data | any collection of numbers, characters, images, or other items that provide information about something |
| Describing Studies/Data: WHO | describe surveyed individuals |
| Describing Studies/Data: WHAT | determine what is being measured |
| Describing Studies/Data: WHEN | when the research was conducted |
| Describing Studies/Data: WHERE | where the research was conducted |
| Describing Studies/Data: WHY | the purpose of the survey/experiment |
| Describing Studies/Data: HOW | how the survey/experiment was conducted |
| A Sample Is Used To Make Inferences About... | a population |
| Simple Random Sample | every individual and every possible group of a specific size has an exact, equal chance of being chosen (ex: names in a hat) |
| Stratified Random Sample | a probability sampling method that divides a population into distinct, non-overlapping subgroups before drawing a random sample from each group |
| Convenience Samples | non-probability research method where data is collected from people or units who are the easiest to access, close by, or readily available |
| Snowball Sample | existing study participants help researchers find and refer additional potential subjects from their social networks (type of convenience sample) |
| Voluntary Response | individuals choose to participate in a study or survey on their own accord (type of convenience sample) |
| Whenever Our Variable Is A Sample Of Measurements From A Population, It Is A... | random variable |
| Random Variables Can Contain Error | examples: response time, behavior |
| Fixed Variables Contain No Error | whether a participant takes medicine or a placebo |
| Parameters | numbers that help specify the model, describes an entire population |
| Statistics | numbers that summarize the data, describes a sample |
| Qualitative Data | contain information about discrete category membership of the observations (categorical or ordinal) |
| Nominal Variable | qualitative variable, describes groups or labels with no inherent order |
| Ordinal Variable | qualitative variable, type of categorical variable that possess a ordered ranking |
| Categorical Data | a data type that assigns an individual or item to a specific group, category, or label rather than giving a measurable number |
| Quantitative Data | contains measured numerical values which may vary continuously (ex: GPA) |
| Interval Variable | quantitative variable, measured along a scale with each point placed at an equal distance from one another |
| Ratio Variable | quantitative variable, measured along a scale with each point placed at an equal distance from one another AND has a true zero |
| Frequency Table | a table with an initial column displaying each outcome & the second column displaying frequency |
| Relative Frequency Table | a table with an initial column displaying each outcome & the second column displaying that outcomes percentage |
| Bar Graphs Are Formatted With A Gap In Between Bars, But Histograms... | do not have gaps (show continuity) |
| Contingency Table | displays two categorical variables' outcomes & their relationship |
| Theory | principle or body of interrelated principles that purports to explain or predict a number of interrelated phenomena |
| RETURN TO DEFS, HISTORY, & STATS VID | *in my notes* |
| Histograms | a chart that displays the distribution of quantitative data |
| Relative Frequency Histogram | percentages are on the y-axis |
| Multimodal Histogram | a graph with three or more distinct peaks that shows data coming from multiple separate groups or patterns |
| Unimodal Histogram | a data graph that shows a single, distinct peak or high point where most of the data values cluster |
| Bimodal Histogram | a graphical bar chart that displays data with two distinct peaks or modes |
| The Hump Or Highest-Frequency Bin In A Histogram Is The... | mode |
| Mean, Median Or Mode: Categorical Data | mode |
| Mean, Median Or Mode: Ordinal Data | median |
| Mean, Median Or Mode: Skewed Data | median |
| Mean, Median Or Mode: Quantitative Data | any |
| Mean, Median, & Mode Are Center Values When The Distribution Is... | symmetric |
| Mean, Median, & Mode Are Approximations When The Distribution Is... | asymmetric |
| Variability | how spread out is the data? how far away from the mean or median do the observed values tend to be? |
| Percentiles Divide The Data Into... | 100 groups |
| The nth Percentile | the data value such that n percent of the data lies at or below that value (ex: if the 75th percentile SAT score for freshman is 1400, 75% of student scored a 1400 or below) |
| The 50th Percentile Is The Same As... | the median |
| Quartiles Divide The Data Into... | 4 groups |
| First Quartile Percentage | 25% lower or equal |
| Second Quartile Percentage | 50% (median) |
| Third Quartile Percentage | 75% lower or equal |
| Five Number Summary | the minimum, the 1st quartile, the median/2nd quartile, the 3rd quartile, the maximum |
| Interquartile Range | calculates the difference between the 25th & 75th percentile (the range spanned by the "middle half" of the data) |
| The Interquartile Range May Be Less Influenced By... | outliers |
| Boxplots Extend From... | the true minimum to the true maximum |
| The Center Line Of A Boxplot Is The... | median/2nd quartile |
| The Box In A Boxplot Ends In... | the first quartile & the third quartile |
| The Whole Box In A Boxplot Is Equal To... | the interquartile range |
| Whiskers In A Boxplot End At The... | minimum or maximum value within the fences (excluding outliers) |
| Anything Outside Of The Fences Of A Boxplot Are Considered... | outliers |
| Population Variance | a measure of how far apart a set of numbers is from their average (mean) |
| Population Standard Deviation | a number that shows how spread out data values are from the average (mean) in an entire group |
| When We Want Ti Report Spread Along WIth A Mean, We Use The More Interpretable... | standard deviation |
| Variance | measure of average difference between the data points & the mean |
| Generally, In A Normal Distribution, 68% Of The Data Should Fall Within... | 1 standard deviation from the mean |
| Generally, In A Normal Distribution, 95% Of The Data Should Fall Within... | 2 standard deviations from the mean |
| Generally, In A Normal Distribution, 99.7% Of The Data Should Fall Within... | 3 standard deviations from the mean |
| A High Standard Deviation Occurs When... | most of the values are very different from the mean |
| Parameters Of A Normal Distribution | mean & variance/standard deviation |
| Parameter | any quantity of a statistical population that summarizes or describes an aspect of the population, such as a mean or a standard deviation |
| Positive/Right Skew | most values cluster on the left/are lower |
| Negative/Left Skew | most values cluster on the right/are higher |
| Kurtosis | degree to which a distribution is more or less "peaked" |
| Kurtosis Of A Leptokurtic Distribution | positive kurtosis, higher peak |
| Kurtosis Of A Platykurtic Distribution | negative kurtosis, lower peak |
| Kurtosis Of A Normal Distribution | zero kurtosis |
| How To Tell If A Distribution Is Normal | normal q-q plot |
| Q-Q Plot | compares two probability distributions by plotting their quantiles against each other |
| Z-Scores | how many standard deviations a specific data point is away from the mean of a data set |
| Bernoulli Distribution | distribution referring to a single observation, categorical variables with just two outcomes (special binomial case) |
| Parameters Of Bernoulli Distribution | probability(p) & probability of the alternative outcome (q) |
| Binomial Distribution | a probability distribution that sums up the likelihood that a value will take one of two independent values, categorical data only |
| Parameters Of Binomial Distribution | n (>1) & probability |
| Uniform Distribution | all values are equally likely to occur, applicable to quantitative & qualitative data |
| Parameters Of A Uniform Distribution | minimum & maximum |
| Central Limit Theorem | the sampling distribution of the mean approaches a normal distribution as the sample size increases |
| Sampling Distribution Of The Mean | distribution of the means of multiple samples taken from a population |
| Standard Deviation Of The Sampling Distribution Is Also Called... | the standard error/standard error of the mean (SEM) |
| There Is A Minimum Value For The Standard Deviation Of The Standard Error Of The Mean, & It's Determined By... | the sample size (bigger sample size, lower SD can go) |
| The More Samples You Take, The More The SEM Will... | go down |
| The Lower Bound Of The Standard Error Of The Mean Is Determined By The... | size of each sample |
| Uncertainty In Statistics | the fact that there will always be noise in an estimate from a sample |
| The Sample Mean Is Equal To The... | estimate of the population mean |
| Null Hypothesis | default statistical assumption stating that there is no real effect, difference, or relationship between variables |
| Critical Region | corresponds to those values of X that would lead us to reject the null hypothesis, both tails of the distributions |
| In Order To Reject The Null Hypothesis, X Should Be... | very big or very small |
| In Psychology, Alpha Is Set To... | 0.05 |
| One-Tailed Test | finding values that are more OR less extreme (not both) |
| Two-Tailed Test | finding values that are more & less extreme (critical region can only be half of what alpha is) |
| Critical Values | define the edges of the critical region in either one or both tails of the distribution |
| P-Value | the probability of getting your test results, or something even more extreme, purely by random chance, assuming that nothing actually happened |
| We Reject The Null Hypothesis If Our P-Value Is Less Than... | alpha |
| Effect Size | the distance between the mean from the population your sampling & the mean your comparing to |
| GO BACK TO WEEK 4, VIDEO 3 | :) |
| Research VS Statistical Hypothesis | research: a substantive, testable scientific claim // statistical: mathematically precise & corresponds to specific claims about the characteristics of the population |
| H1 | alternative hypothesis |
| H0 | null hypothesis |
| Alternative Hypothesis | there is an effect |
| In Statistical Hypothesis Testing, We Assume That The Null Hypothesis Is... | true (our data needs to provide enough evidence to reject it) |
| Type 1 Error: False Positive | we find a significant effect in our experiment when there is none |
| Type 2 Error: False Negative | we fail to find a significant effect when there is one |
| β Is The Probability Of Committing... | a type II error |
| α Is The Probability Of Committing... | a type I error (significance level of the test) |
| Inferential Statistics | allows for inferences about the population from the sample |
| Descriptive Statistics | describes the sample |
| Using The Formula n-1 Reduces... | bias |
| Variance Is Just The Standard Deviation... | squared |
| Greek VS Regular Letters | Greek letters are for population, regular letters are for sample |
| Empirical Rule | about 68% of values are between 1 & -1 SD, 95% between 2 & -2 SD, 99.7% between 3 & -3 SD |
| One Sample Z-Test Requirements | known mean & standard deviation of he population you want to compare your sample to |
| Purpose Of A One Sample Z-Test | to compare your sample to a known population |
| Purpose Of Cohen's D | allows us to standardize effect sizes so we can compare across studies |
| Cohen's D Small Effect | d=.2 |
| Cohen's D Moderate Effect | d=.5 |
| Cohen's D Large Effect | d=.8 |
| Order Of A Report | sample mean, population mean, z-score/t-score, p-value, Cohen's d, reject/accept null |
| Assumptions Of The Z-Test | population distribution is normal, observations are independent, the sd of the population is known |
| The Z-Test Uses A Known Population Standard Deviation, While The T-Test Uses... | an estimate of the population sd from the sample |
| Degrees Of Freedom Definition | the number of values that are free to vary in the calculation of a statistic (how many we can do without once we know that statistic) |
| Why Degrees Of Freedom Are Important | they help us define the sampling distribution for the t-test |
| How Heavy The Tails Are Depend On The... | degrees of freedom |
| As The Number Of Degrees Of Freedom Increases, Tail Heaviness... | decreases |
| As The Number Of Degrees Of Freedom Decreases, Tail Heaviness... | increases |
| Law Of Large Numbers | the larger our sample size, the closer our sample estimate will be to the true population value |
| Assumptions Of The One Sample T-Test | population distribution is normal, observations are independent |
Created by:
age47
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