Essential Concepts

IDS Unit 3: Essential Concepts

Lesson 1: Anecdotes vs. Data

Data beat anecdotes. In science, we need to closely examine the quality of evidence in order to make sound conclusions. Anecdotes can contain personal bias, might be carefully selected to represent a particular point of view, and, in general, may be completely different from the general trend.

Lesson 2: What is an Experiment?

Science is often concerned with the question "What causes things to happen?" To answer this, controlled experiments are required. Controlled experiments have several key features: (1) there is a treatment variable and a response variable, and we wish to see if the treatment causes a change that we can measure with the response variable; (2) There is a comparison/control group; (3) Subjects are assigned randomly to treatment or control (randomized assignment); (4) Subjects are not aware of which group they are in (a 'blind'). This may require the use of a placebo for those in the control group; and (5) those who measure the response variable do not know which group the subjects were in (if both 4 and 5 are satisfied, this is a 'double blind' experiment).

Lesson 3: Let’s Try an Experiment!

Randomized assignment is required to determine cause-and-effect.

Lesson 4: Predictions, Predictions

Designing an experiment requires making many decisions, including what to measure and how to measure it.

Lesson 5: Time Perception Experiment

Designing and carrying out an experiment helps us answer specific statistical questions of interest.

Lesson 6: Observational Studies

Observational studies are those for which there is no intervention applied by researchers.

Lesson 7: Observational Studies vs. Experiments

Experiments are not always possible because of various factors such as ethics, cost limitations, and feasibility.

Lesson 8: Monsters that Hide in Observational Studies

Confounding factors/variables make it difficult to determine a cause-and-effect relation between two variables.

Lesson 9: Survey Says…

Surveys ask simple, straightforward questions in order to collect data that can be used to answer statistical questions. Writing such questions can be hard (but fun)!

Lesson 10: We’re So Random

Another popular data collection method involves collecting data from a random sample of people or objects. Percentages based on random samples tend to ‘center’ on the population parameter value.

Lesson 11: The Gettysburg Address

Statistics vary from sample to sample. If the typical value across many samples is equal to the population parameter, the statistic is 'unbiased.' Bias means that we tend to “miss the mark.” If we don't do random sampling, we can get biased estimates.

Lesson 12: Bias in Survey Sampling

Another popular data collection method involves collecting data from a random sample of people or objects. Percentages based on random samples tend to ‘center’ on the population parameter value.

Lesson 13: The Confidence Game

We can estimate population parameters. This means that we can give an estimate “plus or minus” some amount that we are confident contains the true value (the population parameter).

Lesson 14: How Confident Are You?

We can estimate population parameters. This means that we can give an estimate “plus or minus” some amount that we are confident contains the true value (the population parameter).

Lesson 15 Ready, Sense, Go!

Sensors are another data collection method. Unlike what we have seen so far, sensors do not involve humans (much). They collect data according to an algorithm.

Lesson 16: Does it have a Trigger?

A key feature that distinguishes the way sensors collect data from more traditional approaches is that sensors collect data when a 'trigger' event occurs. In Participatory Sensing, this event is something we humans agree upon beforehand. Every time that trigger happens, we collect data.

Lesson 17: Creating Our Own Participatory Sensing Campaign

Creating a Participatory Sensing Campaign requires that survey questions must be completed whenever they are “triggered”. Research questions provide an overall direction in Participatory Sensing Campaign.

Lesson 18: Evaluating Our Own Participatory Sensing Campaign

Statistical questions guide a Participatory Sensing Campaign so that we can learn about a community or ourselves. These Campaigns should be evaluated before implementing to make sure they are reasonable and ethically sound.

Lesson 19: Implementing Our Own Participatory Sensing Campaign

Practicing data collection prior to implementation allows optimization of a Participatory Sensing Campaign.

Lesson 20: Online Data-ing

We stretch students' conception of data, to help them see that many web pages present information that can be turned into data.

Lesson 21: Learning to Love XML

XML is a programming language that we use with our campaigns. We create basic XML "tags" in the code, which help us store data in a format we understand.

Lesson 22: Changing Orientation

Converting XML to spreadsheet format helps us better understand and view our data.