Data Collection to Conclusion and Evaluation Lab Reports

Data Collection: Quantitative vs Qualitative Data Quantitative Data: Measurements of the dependent variable that are collected in each trial Information is usually numeric and would include units Displayed in a table Qualitative Data: Observations of each trial Information is descriptive and would include things like color change, presence of bubbles, or any other things noticed about the trial. Should include specific information. Often displayed in a table, but this may vary. Raw Data - Data that is collected (and not yet changed mathematically)

Data Collection: Tables Tables should have titles and short descriptions Generally, the independent variable will be displayed on the side and the dependent variable will be across the top. Units and uncertainties should be included in the headings (NOT in each of the boxes of the table) The same number of decimals places should be used for each value (in the same column) and this should match the uncertainty http://www.markedbyteachers.com/gcse/science/physics-lab-conservation-of-momentum.html https://ronia.info/pages/s/science-data-tables/

Measurement and Uncertainty To complete labs, we must be able to measure things, but we must also be able to judge whether or not these measurements are reasonable Accuracy refers to the agreement of a particular value with the true or known value. Precision refers to the degree of agreement among several measurements made in the same manner. (How close are they to each other?) Neither accurate nor precise Precise but not accurate Precise AND accurate

What is uncertainty? All measurement equipment has uncertainty. It can be minimized, but it cannot be eliminated. Uncertainty is the level of precision the equipment can measure to. Equipment cannot read to an infinite number of decimal places. A digit that must be estimated is called uncertain. Generally, there is one digit that is considered estimated in each measurement.

How do you determine uncertainty? Most items such as glassware, rulers, etc. will use the ’Half Rule’ Determine the distance between 2 hash marks. Divide this number by 2. Electronic equipment will be determined by the manufacturer Some will be written on the equipment. Others you will need to research. Which balance has the greatest uncertainty in measurement?

How to write uncertainty Uncertainty is written +/- (the value of the uncertainty) (the unit of the uncertainty) Example: +/- 0.05 L When using the half rule, there will be 1 unknown value (decimal place) added to account for the uncertainty For example: A student is using a ruler where the distance between hash marks is 1 cm, the uncertainty is +/- 0.5cm. In this case, all measurements using this ruler should be written with 1 decimal place. Electronic equipment is limited to what values are read and the manufacturers information.

What is the measure of each of the following? Remember everything that is measured should respect the uncertainty. If an uncertainty is +/- 0.5 mL, ALL measures will be written with either 0 or 5 as the final unknown digit.

SI Basic Units & Quantities Length = meter (m) Volume = volume of 1m x 1m x 1m cube = 1 m3 More convenient = liter (L) 1 cm3 = 1 mL 10 drops H2O ≈ 1 mL Mass = kilogram (kg) Defined as the mass of 1 L of H2O @ 4ºC weight is a force that measures the pull on a given mass by gravity

SI Basic Units & Quantities Temperature = Celsius (oC) Water freezes @ 0oC Water boils @ 100oC Kelvin (K) - the absolute scale Don’t use the degree symbol Water freezes @ 273 K Water boils @ 373 K K = oC + 273 oC = K - 273

Data Analysis Data Analysis involves processing data using a variety of math concepts. Statistics allows us to make decisions about a population based on a sample of that population rather than on the entire population. Commonly use average / mean Commonly use standard deviation (especially for the creation of error bars) This is not limited and others are possible depending on the RQ

Data Analysis Once data has been changed mathematically in any way, it is referred to as Processed Data. Processed data is displayed in a table with headings and details similar to those used for collected data. A sample calculation for any math undertaken should also be included in this section. Only 1 sample is required for each type of math. For example: The average is found for each of the 5 changes of the independent variable in a lab. You only need to include 1 example of this, not all 5. A full written description (in words) is not needed unless the calculation is unclear, but the example should stipulate what data is being used.

Standard Deviation Show the average difference each data point has from the mean. The unit is the same as the unit of measure for the data set Shows how much variation there is in a data set. Is a measure of the spread of the most commonly occurring data points Is often used to create error bars on a graph

Standard Deviation Using the empirical rule, we can determine what range values in our data set should be in assuming a normal distribution of data If a distribution follows a bell curve, we would expect 68% of the values to fall within 1 standard deviation 95% of the values to fall within 2 standard deviations 99.7% of the values to fall within 3 standard deviations This information can tell us a lot about the reliability of our data.

Using Excel for Calculations Open Excel and enter data into a column. Activate the box where you would like information to appear. Click on ‘Formulas’. You will most likely choose the button ‘More Functions’ and then click ‘Statistical’ STDEV.S is standard deviation for a sample This is usually the type of SD you will be using STDEV.P is standard deviation for population The number generated is +/- 1 standard deviation.

Data Analysis: Graphs Data (usually processed) will be used to create a graph All Graphs need a descriptive title that includes the independent and dependent variables Label both axes with SI units if appropriate. NO NAKED NUMBERS. Independent Variable belongs on the X-axis Dependent Variable belongs on the Y-axis. Axes should have an appropriate scale, decided by the highest and lowest number. Error bars (A brief description under the graph will detail what information is used for error bar creation)

Types of Graphs It is important to choose the graph that is appropriate given the data collected and research question: Line graph XY Scatterplot Bar graph Histogram Pie chart

Line Graphs Used when: Data is continuous The independent variable is numeric Specific values for the independent variable are chosen, but there is a range of values that could have been chosen between those values Example: Independent variable is the mass of salt used. 0.5,1.0, 1.5, 2.0 and 2.5 g are chosen, but you could have chosen any value between those. Example: Independent variable is time

XY Scatterplot Used when: Both the independent and dependent variables are measurable. These two measurements are then graphed like a grid to determine trends. Data is numeric and continuous. Points on the XY scatterplot are not generally connected. Instead, a line a best fit is used to determine prevailing trends

Bar Graph Used when: Data is not continuous. Data is in categories Data may not be numeric (but it can be, especially if a range is used) The bars of the graph should not touch (as data is not continuous)

Error Bars Error bars are added to a graph to represent the precision / spread of the data Small error bars indicate: Data collected for a particular independent variable is similar The range of the data is small We are more likely to trust the results. Large error bars indicate: Data collected for a particular independent variable is spread across a larger range We are less likely to trust the results.

Error Bars Overlapping error bars Indicate that values are similar and there is NOT a significant difference in averages and data sets The difference between means is most likely due to chance Error bars represent the standard deviation of the data sets

Error Bars Non-overlapping error bars Indicate that values are NOT similar and there MAY BE a significant difference in averages and data sets A T-test must be done to determine if it IS significantly different. Error bars represent the standard deviation of the data sets

Error Bars Error bars can be formed using: Standard deviation Must have at least 5 trials for this to be used This will most likely be completed in Excel (maximum value-minimum value)/2 Only used when fewer trials are completed

Reading the graph Which data set (type of food) seems to be the most reliable and why? Between which type of food does there seem to be a significant difference in the growth of fish? and explain why you made that conclusion?

Conclusion Explains the results of the investigation Fully discuss the data This should include quantitative and qualitative raw data, processed data and graphs. Describe the trend that is suggested given the data Comment on the hypothesis Supported, not supported/refuted, inconclusive Words to avoid: true/false, right/wrong, correct/incorrect Sentence starters can be found in ‘Science Lab Report Format’

Evaluation To what extent can our lab results be trusted? This section focuses on: Validity - the success of the method in measuring what you wished it to measure based on the RQ Consider control variables, manipulation of variables, equipment, short-comings in the method, etc. Reliability – the ability of the measurements taken to reasonably answer the RQ (precision and accuracy are related) Consider size of the sample, error, uncertainties, accuracy, techniques, etc.

Evaluation Limitations – To what extent can we assume this result can be replicated? Is there anything else that could have caused this result or that would make our results less reliable? For each of the issues noted in the evaluation, a specific, realistic, detailed improvement must be given.