Download
what is the nature of science n.
Skip this Video
Loading SlideShow in 5 Seconds..
What is the Nature of Science? PowerPoint Presentation
Download Presentation
What is the Nature of Science?

What is the Nature of Science?

585 Vues Download Presentation
Télécharger la présentation

What is the Nature of Science?

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. 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.

  2. 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.

  3. 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.

  4. 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!

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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!

  10. 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.

  11. Analysis • Anything done to the data is analysis. • Analysis includes: • graphing • identifying trends • making calculations • estimating amount and types of error, etc.

  12. 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.

  13. 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)

  14. 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!

  15. 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)

  16. 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)

  17. Making calculations - 2 What is the slope of this line?

  18. 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

  19. 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.

  20. 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

  21. 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 %

  22. 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!