Chapter 3: Scientific Data & Analysis

Front 1

Measuring Mass

Back 1

Gram

Front 2

Measuring Volume

Back 2

Liter

Front 3

Density

Equation

Back 3

Density = Mass/Volume

Front 4

Meniscus

Back 4

This is the upward or downward curve at the surface of a liquid in a container.

Concave Meniscus: curves in because the molecules being more attracted to the wall of the container than each other --- read at bottom

Convex Meniscus: curves upward because the molecules are more attracted to each other than the wall of the container -- read at top

Front 5

Dimensional Analysis

Factor-Labor Analysis

How many cm's are in 2.3 miles?

Back 5

2.3 mile * 5,28ft/1 mile * 12in/1ft * 2.54cm/ 1in = 370,000cm

Front 6

Qualitative

Back 6

Meaning that it is based on an observation, but it's not directly measured and recorded numerically

Front 7

Quantitative

Back 7

Meaning that it is based on a measurement, and it's reported numerically

Front 8

Significant Figure

Back 8

Is a number that plays a role in the precision of a measurement.

Front 9

Calculations and Significant Figures

Multiplying 40 X 9.2

Back 9

When multiplying and dividing, your answer must have the same number of significant figures as the number with the least number of significant figures. For example, if you are multiplying 40 (with one significant figure) and 9.2 (with two significant figures), your answer should contain only one significant figure, so it should be reported as 400 instead of the 368 your calculator would give you.

Front 10

Metric System

Back 10

Front 11

SI Units

Back 11

Front 12

Measures of Central Tendency

Back 12

A set of descriptive measures that indicate the typical score. When conducting research, we need or want to know what is most likely going to happen.

Front 13

Mean

Back 13

As the data's average score of the sample

When other statistical tests fail and you can't use them, you rely on the mean because it has 50% of the scores above it and 50% of the scores below it. It's just the one that's most likely to happen.

Front 14

Median

Back 14

The middle score after the scores have been arranged in numerical order

Medians are useful for understanding what the central part of the data looks like. If there are a lot of lows and highs, then this score will be least affected by it because all you do is count to the central most number where the scores are in numerical order. If there are two central numbers, you simply average the two.

Front 15

Mode

Back 15

The most often occurring value

Mode likely has the weakest link to population due to its sampling bias. It can be reported to give the reader of your research an understanding of what the average score looked like. This may be important if your mode was different than the average. If your mode was four cats and your average was one cat, that means something very strange is happening.

Front 16

Variance

Back 16

Is the average of each distance from the mean squared

Front 17

Standard Deviation

Back 17

The measure of how closely all of the data in the data set surrounds the mean

Front 18

How to Calculate the Standard Deviation?

Back 18

1. Find the mean of your data set
   
2. Subtract the mean from each of the data points
   
3. Take each of the differences and square them
   
4. Find the variance, which is the average of the squared differences
   
5. Calculate the square root of the variance, which is the standard deviation

Front 19

How to find the Range for two Standard Deviations

Back 19

You take the mean and +/- the Standard Deviation

Front 20

Human Error

Back 20

Includes any basic human mistake: spilling a substance, dropping equipment, forgetting to turn off the drying oven, etc. These errors can and will happen, no matter how avoidable they may seem in hindsight!

Front 21

Random Error

Back 21

Which is error that occurs randomly in space and time.

This type of error occurs periodically and in no specific pattern and, therefore, gives you imprecise results.

Random errors are unavoidable and are just part of every dataset, but they are just as likely to occur in a positive or negative direction.

This means that theoretically, after you take enough measurements, the random errors would essentially cancel themselves out, getting you closer to your 'true' value

Front 22

Systematic Error

Back 22

Which is error associated with the instruments

Perhaps your pipette is not dispensing the correct amount of fluid, or your scales are not calibrated correctly

Front 23

Drift

Back 23

This is when an instrument gradually changes over time.

Drift often occurs in instruments that record continuously, like detectors

Front 24

Accuracy and Precision

Back 24

Accuracy refers to how close a measurement is to the 'true' value

Precision, often called reproducibility or repeatability, is the agreement of repeated measurements

Front 25

Descriptive Statistics

Back 25

It describes the existing data, using measures such as average, sum and others

When you say you are going out for dinner on average twice per week, you're using descriptive statistics.

Front 26

Inferential Statistics

Back 26

Tries to infer something about the data.

In other words, you are trying to find patterns and relationships in the data.

Front 27

Predictive Statistics

Back 27

Tries to predict future conditions based on existing data.

Front 28

Bar Gragh

Back 28

A graph with bars of varying heights, is a good visual way to represent the categorical data from a survey for analysis

The thing about categorical data is that because they are groups, there is not much to say about other groups that aren't listed on the graph. If you don't have data for other groups, there is not much you can predict about them

Front 29

Scatter Plot

Back 29

Is a visual representation of the relationship between two variables

Best way to represent quantitative data is with a scatter plot -- can be used to make predictions (linear relationship)

Front 30

Correlation Relationship

Back 30

A numerical representation of the strength and direction of the relationship

If there is a close relationship, meaning that as one moves, so does the other, then the coefficient is closer to 1 or -1.

Remember that if the coefficient is negative, all this means is that when one variable increases, the other variable decreases.

A coefficient closer to 0 means that there is less or no relationship between the variables.

Front 31

Internal Validity

Back 31

Is the extent to which a researcher can say that only the independent variable affected the dependent variable

Low external validity

Few extraneous variables

Front 32

External Validity

Back 32

The extent to which a study reflects the real world -- not done in a lab (most likely)

Low internal validity

Lots of extraneous variables

Front 33

Back 33