Hypothesis
If the cost of a microwave popcorn brand is higher then it will yield fewer un-popped kernels.
Purpose
The purpose of this investigation is to assess which brand of microwave butter flavoured popcorn yields fewer un-popped kernels. This question had the need to be answered from both general interest on popcorn itself in terms of why it pops, why some kernels don't pop and it's best popping conditions and the other reason being, to find out which popcorn I should purchase in future, as I believe un-popped kernels represent un-wanted value for your money.
Analysis
To analyse my data , I have answered a Data Analysis Checklist and hav evaluated on it following it.
There is sufficient data to know whether my hypothesis is correct.
The data that I have collected in my experiment is indeed accurate. As the methods of collecting data are both reliable and valid , ensuring data I attain from carrying out this experiment will be accurate.
I have summarised my data with an average where appropriate , I have clearly executed this in my second table (Table 2) of my results , as 3 brands of butter flavoured microwave popcorn were tested 3 times each and in order to organise results into a form which was able to be graphed .
My Chart does specify units of measurement for all data , as all quantitative data was sorted into two tables with appropriate units of measurement (Table 1 and Table 2) found in the Results Section of my experiment. Additionally , the graphs use the unit of measurement of percentage for both graphs.
I have verified that all calculations are correct. All calculations were fulfilled with a Scientific calculator and checked 3 times , in order to make sure mistakes were not present in the data. Any calculations that were not whole numbers were rounded to the nearest whole number.
Evaluating my method
Once my experiment was completed , before I wrote up my conclusion, I reflected over my method of collecting data. In the beginning of this task in week 3, I have stuck with my method of collecting data which was as stated below.
The method I will be using to collect data for the purpose of this study will be that any single kernel that either fits in the criteria of either being:
- Partially broken
- unbroken but inedible
- Not fully popped
Kernels that fit in this criteria will be considered un-popped. Other kernels must be fully popped to be counted as a popped kernel. Once all the bags are popped, I will then individually count the popped and un-popped kernels, placing the un-popped kernels in a small bowl and the popped kernels in a larger bowl. After this I will record the raw data onto two separate data tables on a data sheet.Finally to collaborate my results, I divide the total number of popped kernels in each brand by the total kernels for each brand, obtaining a percentage of popped kernels produced by each brand to graph. By counting the total amount of both popped and un-popped kernels I can easily account for any difference between each brand. Using the measurement of percentages of popped kernels will allow myself to have a more accurate comparison of the brand's overall performance. After much deliberation between constructing a pie graph or column (bar) graph , I have decided to go ahead with the column graph to record my results as not only are they easy to understand , because they consist of rectangular bars that differ in height or length according to their value, They also will be easier to identify any trends and patterns in the results. . This method will be easy to follow along with and repeat.
But, at around week 2 , when I was planning out what methods of collecting data , It was hardly what I stated above. It was a lot simpler and didn't have as much thought in relations to making sure I got the best results possible to identify any trends and patterns in the results that I can use for my discussion and conclusion after the experiment was carried out , it also lacked reliability and validity.
 |
| This was my very first draft of what I had in plan for my "method of collecting data". |
At about Week 3 within the SRP project , when I started to construct my Scientific Method and designing my experiment, I had a clearer view of what was required of my experiment and it's requirements. I then took my ideas from week 2 and collaborated them into a computerised copy of my "method of collecting data".
 |
| Column graph template for experiment |
BRAND | POPPED KERNELS | UN-POPPED KERNELS | TOTAL KERNELS |
Uncle Toby’s 1 | |||
Uncle Toby’s 2 | |||
Uncle Toby’s 3 | |||
Poppin 1 | |||
Poppin 2 | |||
Poppin 3 | |||
Coles 1 | |||
Coles 2 | |||
Coles 3 |
But towards the end of week 3 of My SRP , I had come to a problem. The table didn't collect all the data I needed. So I decided to create a second table which I would fill out once I had finished collecting my data from the first table which was named "Table 1" and the one I was creating would be , "Table 2". Additionally , I also had a think about the graph I was using . As I was using the graphing program to make the template of my Column Graph , I experimented with other graphs to see if it would do better off with my experiment. I saw the "Stacked Column Graph" and looked up on their purpose, which is to compare contribution of two or more variables to a total , across X-azis categories or time. I planned on using a 100% stacked column graph plot option, where the values selected variables will be normalised and scaled to 100 %. I planned to use this type of graph to show the percentage of average popped kernels to un-popped kernels when put over it's average total.I was proud of my new discovery and thought this would be really helpful in comparing results.
 |
| Stacked Column Graph Template for experiment |
BRAND | AVERAGE NUMBER OF POPPED KERNELS | AVERAGE NUMBER OF UN-POPPED KERNELS | AVERAGE TOTAL KERNELS | TOTAL PERCENTAGE OF POPPED KERNELS(%) |
Uncle Toby’s | ||||
Poppin | ||||
Coles |
I had finally come up with my method of collecting data and was very satisfied with it and was ready to use these methods for my experiment , and they were deemed successful and have been put into my report.
As for my scientific method, I have done a lot of progression work on it. I had problems with it in the start , where I had about 10 steps which definitely did not include all steps required to successfully achieve the experiment with ample results. At the start my Scientific method did not start each step with a "verb" which was a disadvantage to someone trying to carry out the experiment , so I imagined that when I read out my Scientific method , I was instructing these steps to someone else carrying out the experiment , this really helped me to see if I had to alter my method in anyway for it to be easy to understand for everyone. In the end I came up with a 15 step Scientific Method which executed the experiment in a safe and successful way , where I got ample results.
From all of this I have learnt that selecting the right method involves many factors. Some methods are better for gathering quantitive data, others for qualitative data. Some are better for particular audiences than other. Some Methods gather riches, deeper data than other do.
Final Scientific Method
1.Make sure you have all your equipment prepared to use
2.Prepare data sheet
3.Choose one bag of popcorn and remove from plastic packet and dispose in bin.
4.Unfold popcorn bag and place flat down on microwave plate.
5.Close microwave door.
6.Set timer to 2 minutes and press start.
7.Wait until times goes off and open microwave door.
8. Time 10 minutes from when you open the microwave door using a timer or stopwatch.
9.Once this time is up , Take out popcorn bag from microwave and open the bag.
10. Sort the popcorn individually into either the small bowl for the un-popped or the big bowl for the popped popcorn.
11.Count how many un-popped kernels and popped kernel there are and record into the first table (table 1)
12.Repeat steps 3-11 for the remaining bags of popcorn
13. Calculate the average number of popped, un-popped, and total kernels for each brand of popcorn by adding the data recorded for that brand and record in second table (table 2), after all bags are popped and recorded.
14.Divide the number of popped kernels by the number of total kernels to find the percentage of popped kernels for each brand and record results in the second table (table 2) and graph results in column graph and stacked column graph like the ones listed below.
15.Compare Results.
My discussion will be in my next post.
BUBBL.US UPDATE:
No comments:
Post a Comment