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What is the Nature of Science?. The Nature of Science is a logical, sequential way of investigating our world. We wonder, what would happen if I …? Then we devise a scientific investigation to explore this idea. Scientific investigations have required parts, and a required order. Variables.

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what is the nature of science
What is the Nature of Science?
  • The Nature of Science is a logical, sequential way of investigating our world.
  • We wonder, what would happen if I …?
  • Then we devise a scientific investigation to explore this idea.
  • Scientific investigations have required parts, and a required order.
variables
Variables
  • Variables are the components that change in a scientific investigation. The components must be measurable. There are 2 types of variables:
    • The independent variable is the component that the investigator changes. It is graphed on the x axis. There is only 1 independent variable.
    • The dependent variable is the component that changes due to the independent variable. It is graphed on the y axis. There is only 1 dependent variable.
constants
Constants
  • In a valid scientific investigation, we change 1 variable (independent) and measure the effect on 1 other variable (dependent).
  • All other components must remain the same!
  • Components that don’t change in a scientific investigation are called constants.
constants 2
Constants - 2
  • For example, we might investigate how amount of sunshine affects plant growth.
  • We would change the daily amount of sunshine (independent variable) and measure the amount of plant growth (dependent variable).
  • What would some constants be?
  • Amount of water, type of plant, type of soil, temperature of the environment, etc – all must stay the same!
control
Control
  • But we would also need to know if sunshine affects plant growth at all, so we need a control – in which we measure the dependent variable when the independent variable = 0.
  • For this experiment, the control would be the amount of growth for a plant with no daily sunshine.
hypothesis
Hypothesis
  • A hypothesis is a statement that links the independent to the dependent variable.
  • It is often written in this form: If the independent variable does this, then the dependent variable will do this.
hypothesis 2
Hypothesis - 2
  • For our earlier experiment (amount of sunshine and plant growth), an acceptable hypothesis would be:
  • If the amount of sunshine increases, the amount of plant growth will increase.
hypothesis 3
Hypothesis - 3
  • What would be another valid hypothesis?
  • If the amount of sunshine increases, the amount of plant growth will decrease.
  • Or
  • If the amount of sunshine increases, the amount of plant growth will remain unchanged.
hypothesis 4
Hypothesis - 4
  • 2 purposes for a hypothesis:
    • To get you thinking about the experiment
    • To get you invested in the outcome
  • A hypothesis is NOT judged on correctness – it is unacceptable to go back and change your hypothesis to reflect what actually happened!
slide10
Data
  • Data is collected through observation – using 1 or more of the 5 senses.
  • Examples of observation:
    • seeing the volume in a graduated cylinder
    • smelling the sulfur odor from a chemical
    • hearing the tick of the metronome, etc.
analysis
Analysis
  • Anything done to the data is analysis.
  • Analysis includes:
    • graphing
    • identifying trends
    • making calculations
    • estimating amount and types of error, etc.
graphing
Graphing
  • Types of graphs and common uses:
  • A circle graph is for percentages.
  • A bar graph is for data that occurs in categories (grades, months, m/f, etc) – called “discrete” data.
  • A line graph is for continuous data.
graphing 2
Graphing - 2
  • A correct line graph has:
  • a relevant title,
  • each axis is labeled including units,
  • each axis has a consistent scale,
  • points are plotted,
  • a line or curve of best fit is drawn (going thru as many points as possible, and with as many points above the line as below)
graphing 3
Graphing - 3
  • If the data points appear to be linear, graph it as a line of best fit.
  • If the data points appear to be curved, graph it as a smooth curve of best fit.
  • Since we are looking for trends or patterns, very rarely do we “connect the dots” when graphing in science!
identifying trends
Identifying trends
  • Trends are either:
    • Direct relationship – when one value increases the other value also increases

 or 

or a line with a positive slope

    • Inverse relationship – when one value increases the other value decreases



or a line with a negative slope

    • No relationship – either too varied to be determined, or remains constant (a line with 0 slope)
making calculations
Making calculations
  • Suppose your task is to find the density of an object. Your lab equipment can measure mass and volume. You can calculate density as mass/volume. Mass and volume are data, the calculation for density is analysis (since you didn’t directly observe it).
  • Often we graph linear data and calculate the slope of the line.
  • Slope = (y2 – y1)/(x2 – x1)
making calculations 2
Making calculations - 2

What is the slope of this line?

making calculations 3
Making calculations - 3
  • The equation for a line is y = mx + b
  • m is the slope,and b is the y-intercept.
  • What would be the equation for the previous graph?
  • y = (.00625 kgm-2/mm)x + .13kgm-2
  • What is y measuring?
  • What is x measuring?
  • Cucumber yield = (.00625 kgm-2/mm)precipitation + .13kgm-2
estimating error
Estimating Error
  • Measurement errors can be categorized as 2 types:
  • Random – caused by the person making the measurement. Random errors can be reduced by repeating the measurement and taking the average.
  • Systematic – caused by the system or equipment used to make the measurement.
estimating error 2
Estimating Error - 2
  • Ways we will calculate:
  • % error is used when comparing an experimental value to a known, standard theoretical value (such as atomic mass, acceleration due to gravity):
    • % error = (|theo – exp| / theo) x 100
  • % difference is used when comparing 2 experimental values:
    • % diff ={|val 1 – val 2| / [1/2 (val 1 + val 2)]} x 100
  • Handout: Calculating uncertainties for IB
estimating error 3
Estimating Error - 3
  • You found carbon’s mass to be 11.5 amu. Your textbook lists it as 12.0 amu. What is the % error?
  • 4.2 %
  • You measured an object’s mass as 25.7 g and your lab partner measured it as 26.9 g. What is the % difference?
  • 4.6 %
human error activity
Human Error Activity
  • 6 stations each with a designated task
  • Perform each task, record your results
  • For each station, calculate % difference between your value and Mrs. G’s value
  • Calculate an overall average of your differences
  • Don’t turn it in yet! Be ready to share!