Summer Work Review Flashcards
What is an observation?
A feature of a sysem that you know to be true because you can see it immediately without testing it
What is an assumption?
A feature of a system that you assume to be true but that you don’t/can’t test it right away.
What is a hypothesis
A proposed answer to a scientific question
What is a regular hypothesis also known as?
An alternative hypothesis
What is a null hypothesis?
A proposition that an observed difference or correlation between two samples could be purely incidental. Nothing is caused by anything else
Experimental Design
The way that your experiment is set up. Your hypothesis, predictions, experiment design.
(independent variable - the variable that you change
dependent variable - the variable that changes because of the independent variable
Trials - how many will you have? it should be more than one.
control groups - group where everything will stay the same)
Why do we use models?
We may not be able to directly observe something it’s full scale due to its size or because we can’t test certain things on a real world system
Open System
Exchanges everything with the world around it like energy and matter (e.g An ecosystem or a national park)
Closed System
Exchanges only energy with its surroundings (e.g. Earth)
Isolated System
Exchanges almost nothing with its surroundings (this is usually considered to be theoretical)
What is negative feedback?
counteracts a change in equilibrium to make something normal (e.g. your pulse rate drops after exercising)
What is positive feedback?
invokes a change in a system that will take it out of equilibrium (e.g. childbirth or your pulse rate going up after exercising)
Accuracy
How close measurements are to the true or ideal value. Measurements could be all relatively accurate but not close to each other.
Precision
How close repeated measurements are to each other
Quantitative Data
Data that you can count or measure
Discrete Data
Data that doesn’t really have a range and can’t be split up. the Data points somewhat skip around (e.g. integer data like number of children)
Continuous Data
Data that is in more of a range and can be a part of a number(e.g. decimal data such as height)
Qualitative Data
Non-numerical data
Ranked Data
Data that has a specific order
Rate
Demonstrates how a variable changes over time
Probability
how likely something is to happen
Percentages & Fractions
Fraction demonstrates how many parts of a whole are present. Percentages are fractions taken out of 100
Ratio
Give a relative amount of two or more qualities
Logs
transforms data to make it easier to work with.
Exponential growth
When a value grows at a rapidly increasing rate. A graph that demonstrates exponential growth will often resemble a “J” (used to demonstrate population growth)
REMINDER. NOT A QUESTION
Be familiar with the concept of Surface Area and Volume. You don’t need to memorize the equations.
Percent Error Definition
Measures how far an experimental value is from an ideal value using percentage
What is the equation for Percent Error?
((experimental - ideal value)/(ideal value)) * 100
Line Graphs
Plot points on a graph and then connect the dots. Usually used when one variable affects another
Scatter Graphs
Plot the points and then draw a line of best fit through the points. Data is continuous for both variables. Often, there is no dependent variable, but the two variables are correlated
Histograms
Continuous Data demonstrated. Like a bar graph, but the bats are touching. Often used to represent frequency
Bar Graphs
Used for discontinuous/discrete data. There are no dependent or independent variables.
Correlation Vs. Causation
Just because two things are correlated does not mean one caused another.
Mean
The average
Rules:
1. Don’t take the average of two averages
2. Don’t calculate the mean of ratios
3. Don’t take the mean of a measurement that isn’t linear (e.g. PH)
Median
The middle number
Mode
The number that appears the most
Range
Highest to lowest value
Standard Deviation
putting a number to the variability of data. Demonstrated how far it is from the mean.
Why is Random Sampling important?
To eliminate bias within your experiment.
Why is it important to have a large sample size?
The more samples you use, the more accurate your results will become.
