Scientific Method, Lab

Report Format and Graphing

Observation

Scientists identify problem to solve by observing world around them

Ask Questions

Information collected from research, observations in attempt to answer questions

Forming Hypothesis and Making Predictions

Hypothesis - statement that can be tested by observations or experimentation– It is a tentative explanation for

problem/question, educated guess Prediction - expected outcome of test

–

Setting up a controlled experiment

Use controlled experiment to test hypothesis– Experiments are planned procedures

to test hypotheses

Record and Analyze Results

Record data Put data into graphs Analyze data

Draw Conclusions

Does evidence from experiment

support or refute (reject) hypothesis

Publish Results

Allows others to use information, repeat experiments to confirm validity of results, review experimental design

Repeating Investigations

Experimental results should be able to be reproduced because nature behaves in a consistent manner

Theory

Set of related hypotheses that have been tested and confirmed many times by scientists

Controlled Experiments

Involve a Control group and an Experimental group– Control group - all conditions kept the

same, receives no experimental treatment, is the experimental trial without the independent variable

–

Controlled Experiments

Involve a Control group and an Experimental group– Experimental group - group that receives

the experimental treatment–

Variables

Dependent or responding variable - variable that is measured in an experiment, what happens because of the independent variable

Controlled Variables (controls) - other factors that could cause changes in the dependent variable, so the scientist wants to keep them the same or constant, so they don’t cause changes

Controlled Experiments

Experiment should be repeated (replicates) or use a large sample size to verify results

Be sure to test only one factor (independent variable) at a time

Test independent variable at different values if possible

Writing a Hypothesis Often written as an If….then… statement If (my guess is true) then (I do this, then this

should happen) If (hypothesis) then (prediction) If (hypothesis is true) then (independent

variable should have this affect on dependent variable)

If (discuss relationship between independent and dependent variable) then (I do this to independent variable, the dependent variable will change in this way)

Question: Does the amount of light affect how fast a plant grows?

Guess: Plants that receive more light will grow faster Independent variable = amount of light received Dependent variable = increase in growth rate Relationship between independent and dependent variable:

Increase in light exposure will cause an increase in growth rate Prediction: (what will happen to the experimental group that

receives the independent variable): The group of plants grown in more than 12 hours of light will show an increase in mass compared to those grown in less than 12 hours

If (discuss relationship between independent and dependent

variable) then (I do this to independent variable, the dependent variable will change in this way)

Lab Report Format

Before experiment

I. Purpose: What is the purpose of the experiment? Why are we doing the experiment? Background information, research needed to help understand or design experiment, reason leading to hypothesis (theory)

II. Materials:

III. Procedure: Detailed step by step instructions of exactly what you plan to do. (Can someone else use your instructions to repeat experiment)

Include diagram of experimental setup Specifically discuss variables

– Independent – how it will be manipulated, differing levels/amounts/concentrations to be administered

– Dependent – how it will be measured-tool or instrument to be used, units, frequency of measurements, if not a common method of collecting data, a picture or diagram illustrating how data is to be collected

– Controlled variables specifically how they will be regulated/controlled if not already done

Safety precautions/equipment required

IV. Data tables: Blank table to record data. Prepare before experiment. Think about what you will measure, how you will measure it, how long you will measure it, how frequently will you take measurements, and what instruments you will use to make the measurements? Units for data, uncertainties of data (15-20 measurements)

During experiment

Collect and record raw data (what you measured) accurately and neatly into organized data tables

Data Collection and Processing - uncertainties

For most measuring devices, uncertainty is half the place value of the last measured value; ex. 25.5 ºC (± 0.5 ºC)

Rulers have an uncertainty of ±1 of the smallest division; ex. 3.1cm ( ± 0.1cm)

For electronic instruments the value is ±1 unit of the last decimal place; ex. 13.7 g (± 0.1g)

Data Processing

Show and perform necessary calculations (calculate means, standard deviations, rates, standardize measurements (divide by volume or surface area to make equivalent)– Include units, significant figures

After experiment

V. Graphs and Charts: graph data or place in charts to give visual representation of data. This will help to analyze data. Choose correct type of graph to show data, does graph show data the way that you want it to?

VI. Conclusion: Summarize results of experiment (what happened?). Analyze results (why it happened?)

– Analyze data and draw conclusions from results based on reasonable interpretation of data, referring to data when possible

– Explain/justify experimental results–

Evaluating Procedures and Results

Evaluate weaknesses and limitations of design of investigation and performance of your procedure

Focus on systematic errors Is data reliable, or did these weaknesses

and limitations impact your data– Small sample size, important variables not

controlled, data not recorded accurately/reliably

Suggesting improvements

Suggest realistic improvements to identified weaknesses and limitations and should focus on specific pieces of equipment or techniques used

Error Analysis Human error

– Systematic errors

– Affects data the same amount every time (equipment not calibrated, zeroed, worn, procedures incorrect, unreliable)

– Sources usually identifiable, may be eliminated or reduced by changes to the experiment

Random error– Does not affect every measurement taken or affect them in

the same manner (reading of apparatus)– The more trails done, the less of an effect a random error

may have on results– May result from limits of accuracy of the apparatus,

inconsistent recording, natural variations in samples

Graphing Data

GRAPHING

1. Title Graph - short but good descriptive title that clearly tells what the graph is about.

2. Identify the Variables independent variable goes on X axis

(horizontal) or TIME when the effect of the independent variable is measured over time (variable vs. control or different degrees of variable will be shown as different lines on graph

3. Determine the Scale of the Graph - determine scale (numerical value for each square) to best fit the range of each variable. Spread the graph to use the MOST of the available space.

4. Number and Label Each Axis - tells what data the lines on graph represent. Include units.

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5. Plot the Points

6. Draw the Graph - connect dots with lines on continuous data. Show approximate best fit line/curve if appropriate (most graphs of experimental data are not drawn as “connect the dots”

7. Label Lines or Use Legend - if graph shows more then one line/set of data, label line or make a legend/key. Use different marks/colors for different sets of data

Types of Graphs Pie Charts - used to compare parts of a

whole (% of something). Use legend to describe what each slice represents

Line Graphs - Used for continuous data-data that is changing. Used to track changes over time or to measure the effect of one thing on another

Bar Graph (Histogram) - used to compare something between groups. Can be used to show large changes over time. –

X-Y plot (Scatterplot) - used to determine if there is a relationships between things. Used when data points are not related/do not show changes over time/effects

A normal distribution is a very important statistical data distribution pattern occurring in many natural phenomena, such as height, blood pressure, lengths of objects produced by machines, etc.

Normal distributions are symmetrical with a single central peak at the mean (average) of the data. The shape of the curve is described as bell-shaped with the graph falling off evenly on either side of the mean. Fifty percent of the distribution lies to the left of the mean and fifty percent lies to the right of the mean.

The spread of a normal distribution is controlled by the standard deviation–

The standard deviation is a statistic that tells

you how tightly all the various examples are clustered around the mean in a set of data. When the examples are pretty tightly bunched together and the bell-shaped curve is steep, the standard deviation is small. When the examples are spread apart and the bell curve is relatively flat, that tells you, you have a relatively large standard deviation.

The Standard Deviation is a measure of how spread out numbers are, the average distance away from the mean

One standard deviation away from the mean in either direction on the horizontal axis accounts for somewhere around 68 percent of the data.