Scientific Method, Lab Report Format and Graphing

Scientific Method, Lab Report Format and Graphing

Observation Perceive objects/events using senses 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 • Prediction - expected outcome of test • Based on hypothesis

Setting up a controlled experiment • Use controlled experiment to test hypothesis

Record and Analyze Results • Record data • Put data into graphs

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, used to compare experimental group to, helps determine if changes are caused by independent variable, or some other factor

Controlled Experiments • Involve a Control group and an Experimental group • Experimental group - group that receives the experimental treatment, all other conditions kept the same except for the single condition being tested which is called the independent variable

Variables Independent or manipulated variable - factor that is varied or tested in an experiment, what we want to test, condition being changed Dependent or responding 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 • 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) • Hypothesis: Ifplants receiving more light grow at a faster rate, thenthe plants that are exposed to light for more than 12 hours will show an increase in their mass gained

Lab Report Format

Before experiment • 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) • Materials:

III. Procedure: Detailed step by step instructions of exactly what you plan to do. (Can someone else use your instructions to repeat experiment) • 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 • Clearly state how data will be collected

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 • 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?

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 • measurements taken inaccurately, inconsistently • Systematic errors • Affects data the same amount every time (equipment not calibrated, zeroed, worn, procedures incorrect, unreliable) • 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 • Title Graph – • 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 • Dependent variable goes on Y axis (vertical)

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

Plot the Points – • 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” • 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. • Bar Graph - used to compare something between groups. Can be used to show large changes over time. Use legend to tell what each bar represents

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

Histogram • When an investigation involves measurement data, one of the first steps is to construct a histogram to represent the data’s distribution to see if it approximates a normal distribution. • Creating this kind of graph requires setting up bins—uniform range intervals that cover the entire range of the data. Then the number of measurements that fit in each bin (range of units) are counted and graphed on a frequency diagram, or histogram. • If enough measurements are made, the data can show an approximate normal distribution, or bell-shaped distribution, on a histogram. These constitute parametric data.

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. Certain data, when graphed (data on the horizontal axis, amount of data on the vertical axis), creates a bell-shaped curve known as a normal curve, or normal distribution.

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 standard deviation is kind of the "mean distance away from the mean" . • 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.

One standard deviation away from the mean in either direction on the horizontal axis accounts for somewhere around 68 percent of the data. Two standard deviations away from the mean account for roughly 95 percent of the data.

Scientific Method, Lab Report Format and Graphing