<aside> <img src="https://prod-files-secure.s3.us-west-2.amazonaws.com/3e5fd3a2-ed5f-4e76-aa94-04dfff30ac4f/4c8985a9-9031-4e8a-8492-d2e56176ed17/callout.jpeg" alt="https://prod-files-secure.s3.us-west-2.amazonaws.com/3e5fd3a2-ed5f-4e76-aa94-04dfff30ac4f/4c8985a9-9031-4e8a-8492-d2e56176ed17/callout.jpeg" width="40px" />

$\colorbox{f8ffed}{in devin robertson's jersey we trust}$

</aside>

statistics: - science of learning from data of measuring, controlling, and communicating uncertainty. - problem solving with data - science of producing useful data to address a research question, analyzing the resulting data, and drawing appropriate conclusions from the data. $\colorbox{fbca86}{can’t be tied down to one definition like gender fluidity and monogamy}$

data: values measured or categories recorded on individual units of interest

observational unit: individual entities of which data will be recorded $\colorbox{f1daa4}{think a data science case}$

variable: any measurable characteristic of an observation/study

categorical variable: measures information that can be classified into a group of categories $\colorbox{daf7a6}{represents qualitative data}$ let’s recall what qualitative data means.

qualitative data: refers to categorical information that can be classified into groups or categories. it is non-numerical data that describes qualities or characteristics and is typically used for labeling variables without providing numerical measurement. qualitative data is also known as categorical data

numerical variable: measures information that can be naturally recorded on a numerical scale. $\colorbox{f8a9d9}{naturally very related to quantitative data like alabama cousins}$

now as we did before let’s do it again and redefine quantitative data

quantitative data: refers to numerical information that can be measured or counted. it is data that can be expressed numerically, providing measurable and easy-to-understand results. quantitative data is used for statistical analysis and often expressed in graphs, charts, or tables. $\colorbox{fdc9f9}{examples of quantitative data include age, weight, temperature, or the number of students attending a class.}$

$\colorbox{ecebbd}{is it categorical or numerical data?}$

• how many hp books have you read? $\colorbox{ecebdd}{numerical}$
• have you been outside the united states? $\colorbox{ecebbd}{categorical}$
• what would you guess for the instructors age? $\colorbox{ecebdd}{numerical}$
• do you consider yourself an early bird or night owl? $\colorbox{ecebbd}{categorical}$

population: complete collection of elements that are of interest in a given problem - larger group we which to generalize results $\colorbox{c982ad}{related to parameter, examples include wsu students, starbucks workers, sexually active adults, etc.}$

sample: smaller subset of population of which data is collected on

descriptive statistics: methods that show/describe/summarize data in a meaningful way (collected from the sample !!!!)

inferential statistics: methods that allow us to draw conclusions about the larger population that the sample represents

statistical inference: formal process in which a sample is used to make claims about a population

$\colorbox{ecebdd}{descriptive methods}$

• summarize information from only observational units

$\colorbox{ecebbd}{inferential methods}$

• drawing conclusions for the whole population based on evidence of the study

☆ key differences between descriptive and inferential statistics:

• purpose: descriptive statistics summarize and describe the sample data, while inferential statistics draw conclusions about the larger population.
• scope: descriptive statistics focus on the immediate data collected from the sample, whereas inferential statistics extend beyond the sample to make broader generalizations.
• methods: descriptive statistics use measures like mean, median, mode, and standard deviation. inferential statistics employ techniques such as hypothesis testing and confidence intervals.