# data[].attributes.attachment_link.description
Google Forms - create and analyze surveys, for free.

# data[].attributes.headline
observational is looking experimental is stepping in and changing stuff  

# data[].attributes.subject
observational is looking experimental is stepping in and changing stuff  

# data[].attributes.headline
Experimental studies involve active change. Observational studies don't.

# data[].attributes.subject
Experimental studies involve active change. Observational studies don't.

# data[].attributes.headline
Observational is observing data without influencing outcome. Experimental is doing/adding something to influence the outcome

# data[].attributes.subject
Observational is observing data without influencing outcome. Experimental is doing/adding something to influence the outcome

# data[].attributes.body
observation is looking,experimental is changing stuff

# data[].attributes.body
Observational is going off of something as it plays out, Experimental is more so setting up your own "scenarios" for getting your data.

# data[].attributes.body
Experimental studies is when changes happen and Observational studies is when there are no changes.

# data[].attributes.headline
experimental change factors in it while observation is just watching 

# data[].attributes.subject
experimental change factors in it while observation is just watching 

# data[].attributes.body
Experimental studies are studies where someone changes stuff. Observational studies are studies where no changes are made.

# data[].attributes.body
Experimental: when a change occurs

Observational: when a change does not occur.

# data[].attributes.headline
Observational is when researchers only observe and collect data without changing anything, and experimental is when researchers apply a treatment or change something to see its effect. 

# data[].attributes.subject
Observational is when researchers only observe and collect data without changing anything, and experimental is when researchers apply a treatment or change something to see its effect. 

# data[].attributes.body
homemade food

# data[].attributes.body
Bias is when something is always a little off. Like an error that isn’t fair.

# data[].attributes.headline
Bias is favoring one side over another in a way that is not balanced or fair.

# data[].attributes.subject
Bias is favoring one side over another in a way that is not balanced or fair.

# data[].attributes.body
it is a favor or against one thing, person, or a group compared with another

# data[].attributes.body
When something is unfair and not equal

# data[].attributes.headline
Favoring one side over the other, in a way it’s considered unfair.

# data[].attributes.subject
Favoring one side over the other, in a way it’s considered unfair.

# data[].attributes.body
bias is when something is never even on one side.

# data[].attributes.body
Bias is when something is in favor of someone or something else.

# data[].attributes.body
Bias is inappropriate weight in favor of an idea. - Kate Rucker

# data[].attributes.headline
means to be in favor of one thing over the other

# data[].attributes.subject
means to be in favor of one thing over the other

# data[].attributes.body
Home

# data[].attributes.body
experimental is changing

observational is looking

# data[].attributes.body
Prejudice in favor of or against one thing, person, or group compared with another, usually to be considered unfair.

# data[].attributes.body
prejudice in favor of or against one thing

# data[].attributes.wish_content.attachment_props.question
Do you like or dislike doing the warm-ups/closings on Padlet? 

# data[].attributes.headline
An example of Inferential, a company surveys 100 customers about their satisfaction and then uses the results to predict how all their customers feel, and an example of Descriptive is, a teacher finds that the average score in her class on a math test is 85%.

# data[].attributes.subject
An example of Inferential, a company surveys 100 customers about their satisfaction and then uses the results to predict how all their customers feel, and an example of Descriptive is, a teacher finds that the average score in her class on a math test is 85%.

# data[].attributes.headline
favoring one side over another and is not balanced 

# data[].attributes.subject
favoring one side over another and is not balanced 

# data[].attributes.headline
it is something that describes a population like a country 

# data[].attributes.subject
it is something that describes a population like a country 

# data[].attributes.body
Example of Inferential Statistics:

A researcher surveys 100 students at your school and finds that 60% prefer pizza over burgers. They then predict that about 60% of all students in the district prefer pizza.

Example of Descriptive Statistics:

The average test score in your math class is 85%.

# data[].attributes.body
I- 2,964 people to predict the outcome for office using the sample data to make predictions about the population

D- mean, median, mode, range, and standard deviation of test grades

# data[].attributes.body
An example of a descriptive is something more detailed like the average of something. An example of inferential is something like SAT test scores.

# data[].attributes.body
an example of inferential statistics is like using a sample of voters to predict the outcome of an election and an example of descriptive statistics is calculating the average height of students in a class to describe the typical height of the group.

# data[].attributes.headline
making predictions and inferences about a larger population based on a sample 

# data[].attributes.subject
making predictions and inferences about a larger population based on a sample 

# data[].attributes.body
An Observational Study is a type of research that involves observing or collecting data from participants

# data[].attributes.body
Online and social media surveys

# data[].attributes.body
I'll use the study guide.

# data[].attributes.body
A convenience survey is when you ask people who are easy to reach, instead of picking a random group.

# data[].attributes.headline
Doing a survey on the first 10 people who walk into the room

# data[].attributes.subject
Doing a survey on the first 10 people who walk into the room

# data[].attributes.body
first 10 people you see survey them.

# data[].attributes.body
the first 10 people that you see and you survey them

# data[].attributes.body
Surveying the first few people who show up

# data[].attributes.headline
like posting something in public like on a wall in a store

# data[].attributes.subject
like posting something in public like on a wall in a store

# data[].attributes.headline
Bias - weighing in favoritism of something else based off your own feelings

# data[].attributes.subject
Bias - weighing in favoritism of something else based off your own feelings

# data[].attributes.body
social media surveys

# data[].attributes.headline
its like the first 10 people you have to survey them 

# data[].attributes.subject
its like the first 10 people you have to survey them 

# data[].attributes.body
study guide

# data[].attributes.headline
Coffee shop owner wants to know if customers like the new seasonal drink, instead of a survey the owner asks everyone in the line that morning.

# data[].attributes.subject
Coffee shop owner wants to know if customers like the new seasonal drink, instead of a survey the owner asks everyone in the line that morning.

# data[].attributes.headline
making a prediction on a larger population

# data[].attributes.subject
making a prediction on a larger population

# data[].attributes.headline
the very first 10 people you see survey them 

# data[].attributes.subject
the very first 10 people you see survey them 

# data[].attributes.body
Using study guides and notes, and also practicing, so I'm more comfortable with the material.

# data[].attributes.body
id use the study guide

# data[].attributes.body
Making a Google Doc of some sort and sending it out to people.

# data[].attributes.body
Prejudice in favor of or against one thing, person or group

# data[].attributes.body
Look over study guide and notes

# data[].attributes.headline
ob is looking ex is changing and then looking

# data[].attributes.subject
ob is looking ex is changing and then looking

# data[].attributes.body
Chocolate

# data[].attributes.headline
when you favor one side even if its is wrong.

# data[].attributes.subject
when you favor one side even if its is wrong.

# data[].attributes.body
A fast-food restaurant manager records the waiting times for 50 customers during a busy lunch hour. They then use descriptive statistics to calculate the mean wait time (e.g., 5 minutes), the median wait time, and the range of wait times. These statistics simply describe the data that was collected from those 50 customers, providing a clear picture of the wait times for that specific period.

Example of Inferential Statistics

A political polling firm wants to predict how the entire country will vote in an upcoming election. Instead of surveying every person, they conduct a poll of a smaller, representative sample of voters. They then use inferential statistics to analyze this sample data and make a prediction about how the entire population is likely to vote, along with a margin of error, such as stating that 55% of the population will vote for candidate A.

# data[].attributes.headline
doing a survey one the first 11 pepole who walk in

# data[].attributes.subject
doing a survey one the first 11 pepole who walk in

# data[].attributes.body
recces

# data[].attributes.body
yorks

# data[].attributes.body
chocolate

# data[].attributes.body
giggles

# data[].attributes.body
I feel like the test will be really easy.

# data[].attributes.body
I feel good about the test and I'm ready for everything because its not that hard in my opinion and I'll make sure to look over everything and make sure I don't miss anything

# data[].attributes.body
I feel like the test won't be that hard to do and understand.

# data[].attributes.body
I feel very confident in the material that we are learning; I am going to do very well on this test. I can study the notes and Delta Math review to prepare.

# data[].attributes.body
It’s easy, ready to do it tomorrow

# data[].attributes.body
I think it'll be easy, I feel prepared for all of it

# data[].attributes.body
I feel good about the test

# data[].attributes.body
I think it will be very easy

# data[].attributes.body
I feel confident ready to test

# data[].attributes.body
I feel confident in this material. I can review delta math to prepare myself for the test tomorrow.

# data[].attributes.body
its going to be easy