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📚 STAT 100 — Chapters 1–3 Flash Cards FLASH CARD 1 Front: What is an estimate? Back: A number or percentage used to describe something about a population. Example: 75% of students play a sport → 75% = estimate FLASH CARD 2 Front: What is the population of interest? Back: The whole group of people or things you want information about. Example: 75% of high school students play a sport → high school students = population. FLASH CARD 3 Front: What is an attribute? Back: A characteristic or piece of information being measured about an observation. Example: Name, major, GPA, age. FLASH CARD 4 Front: What is an observation? Back: One person, object, place, or event in a dataset. ⭐ Usually, one row = one observation. FLASH CARD 5 Front: In a tidy data table, what does each ROW represent? Back: One observation. FLASH CARD 6 Front: In a tidy data table, what does each COLUMN represent? Back: One attribute. FLASH CARD 7 Front: What is quantitative data? Back: Data that represents a number or amount. Examples: GPA, age, price, height, wind speed. FLASH CARD 8 Front: What is categorical data? Back: Data that places something into a group or category. Examples: major, eye color, blood type, ocean. FLASH CARD 9 Front: Is blood type quantitative or categorical? Back: Categorical. A, B, AB, and O are categories, not amounts. FLASH CARD 10 Front: What is a rating scale? Back: Categories that have a meaningful order. Example: Poor → Fair → Good → Excellent. FLASH CARD 11 Front: What is text data? Back: Written words or responses. Example: A student writing why they enjoyed a class. FLASH CARD 12 Front: What is time-series data? Back: Data collected or measured over time. Example: Google’s stock price each day. FLASH CARD 13 Front: What is a measurement? Back: The way an attribute is measured or collected. FLASH CARD 14 Front: What is reliability? Back: How much you can trust a measurement to give dependable information. FLASH CARD 15 Front: What is data validation? Back: Rules used to prevent invalid data from being entered. Example: If GPA must be 0–4, don’t allow someone to enter 40. FLASH CARD 16 Front: What is data cleaning? Back: Finding and handling invalid or incorrect values AFTER data has been collected. FLASH CARD 17 Front: Validation vs. data cleaning? Back: Validation = PREVENT bad data. Cleaning = deal with bad data afterward. FLASH CARD 18 Front: What is a generalization claim? Back: Using information from a sample to make a claim about a larger population. FLASH CARD 19 Front: What is a causal claim? Back: A claim saying one thing causes another thing to change. FLASH CARD 20 Front: What is a causal factor? Back: The thing believed to cause the change. Example: “Eating breakfast improves grades.” → Eating breakfast = causal factor. FLASH CARD 21 Front: What is an outcome? Back: The thing that is affected or changed. Example: “Eating breakfast improves grades.” → Grades = outcome. ⭐ QUICK MEMORY CARD Front: What should I remember most for Chapters 1–3? Back: Estimate = NUMBER Population = WHO Attribute = WHAT Observation = ROW Attribute = COLUMN Quantitative = AMOUNT Categorical = GROUP Time series = OVER TIME Reliability = CAN I TRUST IT? Validation = PREVENT bad data Cleaning = FIX/REMOVE bad data afterward

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Durability bias: the tendency to overestimate how long our emotional reactions (positive or negative) will last. • Impact Bias: the tendency to overestimate the emotional impact of a future event both in terms of intensity and its duration • Misconstrual: errors in predicting emotions because people misrepresent or misimagine the event itself. Inaccurate Theories: errors in predictions about events due to mistaken predictions about the emotional consequences of those events. Can be due to personal experiences or cultural scripts. Motivated Distortions: predictions are biased because people want certain outcomes and distort their mental simulations accordingly, in an overly positive or negative way • Undercorrection: even when you know you may have a bias but not adjusting enough and remaining systematically off in your forecast. Focalism: the tendency to focus too much on the event in question and neglect others experiences that will also influence future emotions. Immune Neglect: failing to recognize how effectively the psychological immune system (defense mechanis as rationalization, reappraisal, denial, etc.) will help us recover from negative events.

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Variable – Anything that can have different values or change. Testable hypothesis – A prediction that can be tested to see if it is supported or proven wrong by observations. Operational definition – A clear explanation of exactly how a variable will be measured or tested. Dependent variable – The variable that is measured or recorded during an experiment. Independent variable – The variable that the researcher changes or controls in an experiment. Population – The entire group of people or things that a researcher wants to learn about. Sample – A smaller group chosen from the population to be studied. Random sampling – A method where every member of the population has an equal chance of being chosen for the study. Case study – A detailed study of one person. External validity – How well the results of a study apply to real life and other people or situations. Demand characteristics – Clues that tell participants what the researcher expects or wants them to do. Double-blind design – A study where neither the participants nor the researchers know who is in each group. Correlation – A relationship between two variables that change together. If one increases and the other increases, it is a positive correlation. If one increases while the other decreases, it is a negative correlation. Validity – How well a test or method measures what it is supposed to measure. Quasi-experiment – A study that compares groups that already existed instead of groups created by the researcher. Correlational studies – Studies that look at how variables are related without changing or controlling those variables. Experiment – A study where the researcher changes one variable to see if it causes a change in another variable. Experimental manipulation – When a researcher purposely changes the independent variable to see what happens. Experimental group – The group that receives or experiences the change made by the researcher. Control group – The group that does not receive the experimental change. It is used for comparison. Random assignment – Randomly placing participants into different groups so the groups are similar at the beginning of the experiment. Internal validity – How sure we can be that the independent variable caused the change in the dependent variable. Replication – Repeating a study or experiment to see if you get the same results. Informed consent – When a person agrees to participate in a study after being told what the study will involve. Debriefing – When the researcher explains the purpose and details of the study to participants after the experiment is finished. IRB (Institutional Review Board) – A group that reviews research involving people to make sure their rights, safety, and well-being are protected.

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