C683 Natural Science Lab, catalog number SCIE 1001, is the two-CU general education laboratory requirement in which you design and carry out your own experiment in the natural sciences and gather quantitative data from it. That sentence contains the whole difficulty. Most science courses hand you a procedure; this one hands you a blank page. The students who struggle are almost never the ones who cannot do science. They are the ones who chose a question that could not produce numbers.
Why the question you pick decides the grade
An experiment you design yourself has one failure mode that dominates all others: the design does not generate quantitative data. A student decides to investigate whether plants grow better with music, sets up two plants, looks at them for a fortnight and writes that one seemed healthier. There is no measurement in that study, so there is nothing to analyse, and every scored aspect downstream of data collection has nothing to work with.
The fix is to test the design against three questions before anything is set up. What exactly will I measure, in what units. How many times will I measure it. What would a result that contradicts my hypothesis look like. A design that cannot answer all three is a design that will produce a submission you have to redo, and the redo costs weeks because the experiment itself takes real time.
The second thing the course tests is honest reporting. A self-designed experiment usually produces messy results, and students who believe an experiment is supposed to work will quietly reshape their findings toward the hypothesis. Scientific writing rewards the opposite instinct. A study that found no effect, reported cleanly with a discussion of why, is a stronger submission than a study that found a dramatic effect nobody could reproduce. Controlling variables, acknowledging error and stating limits are the actual content of the course.
Reading your rubric before you design anything
WGU keeps scored detail inside your Course of Study rather than in the public catalog, and in this course reading it first is not merely good practice, it is the difference between one attempt and three. The scored aspects tell you what your experiment has to be capable of producing. Designing first and reading the rubric afterwards is how students end up with a study that cannot support an aspect they were always going to be scored on.
The scoring rule is standard across WGU: each aspect stands alone against a three-point scale, and a score of 2 in every aspect is what passes a task. Nothing averages. A beautifully written background section does not compensate for a data set with three points in it.
The word budget, worked. Suppose your rubric lists six scored aspects and the directions ask for roughly 2,000 words. Reserve 130 for an opening that states the question and hypothesis, and 110 for a conclusion, leaving 1,760 for the scored body. Six into 1,760 is about 293 words per aspect. Two of those aspects deserve deliberate overweight in a lab report: the methods aspect, which should be detailed enough for a stranger to repeat the study, and the analysis aspect, where the numbers are actually interpreted. Give each of those around 400 and take the difference from the background and conclusion aspects, which reach their ceiling quickly.
Plan the calendar as carefully as the words. A self-designed experiment has a physical duration that no amount of writing speed can compress. Decide your measurement schedule first, count the days it needs, then work backwards from the date you want the course closed.
A structure for a self-designed lab report
Where the task directions specify their own arrangement, follow theirs precisely. Where they leave it open, the conventional scientific report order below is what evaluators expect and what makes each aspect easy to locate.
| Section | What it must contain | Where students lose the point |
|---|---|---|
| Question and hypothesis | A testable statement with a predicted direction, not a topic of interest | Hypotheses phrased so vaguely that no result could contradict them |
| Background | What is already known, cited, and why this question is worth asking | Uncited general knowledge padding out the section |
| Variables | Independent, dependent and controlled variables named explicitly with units | Controlled variables listed vaguely or omitted entirely |
| Method | Materials, procedure and measurement schedule, written so it could be repeated | Procedures written as a narrative of what you did rather than instructions |
| Results | The data, in a table, plus a figure, with no interpretation mixed in | Conclusions smuggled into the results section |
| Analysis | What the numbers show, including spread and any comparison performed | Restating the table in sentences instead of analysing it |
| Limits and next steps | Sources of error, what you would change, what the study cannot claim | Treating this section as an apology rather than as scientific reasoning |
Keeping results and analysis genuinely separate is the habit that most improves these reports. Results are what happened. Analysis is what it means. Students who merge them produce a section where an evaluator cannot tell measurement from opinion, and both aspects suffer.
Evidence craft when you generated the evidence
This course is unusual because most of your evidence is your own. That raises the standard for how it is reported rather than lowering it, since a reader has no independent way to check anything you did not write down.
- Record raw data as you collect it, with date, time and conditions. Data reconstructed from memory afterwards is not data and usually shows.
- Report every trial, including the ones that went wrong, and say what you did about them. Silently dropping an inconvenient run is the one integrity failure this course can actually detect.
- Give units and precision on every measurement, and state the instrument. A length measured with a ruler and one measured with callipers are different evidence.
- Use a figure that answers the question. A chart type chosen because it looks impressive rather than because it shows the comparison is a wasted page.
- Cite background sources properly in the style your directions require. The background section is where uncited claims accumulate fastest.
- Quantify uncertainty in plain terms if statistics are beyond the scope. Reporting a range and the number of trials is more honest than a single average presented as fact.
The mark of a strong report here is a limits section that a scientist would recognise. Sample size too small to generalise, one uncontrolled variable you noticed too late, a measurement instrument coarser than the effect you were looking for: naming these accurately shows you understood your own study, and rubric aspects about evaluation reward exactly that.
What separates Competent from a return
Aspects are scored independently, so returns on lab reports tend to be surgical. The commonest cause is a methods section that describes what happened rather than instructing how to repeat it.
- Every scored aspect has a section of its own, using the rubric's own heading language.
- The hypothesis is falsifiable and the results genuinely bear on it, whichever way they came out.
- The method could be followed by someone who was not there, with no missing quantity or interval.
- Data appears in a table with units, and the figure shows the comparison the question asked about.
- Analysis draws only conclusions the data can carry, and the limits section says what it cannot.
Performance assessment work at WGU can be revised and resubmitted with no grade penalty, and in this course the arithmetic of a return is harsher than usual. A writing fix takes an evening. A design fix means running the experiment again, which is days or weeks of real time inside a six-month flat-rate term where closed courses are the only number that counts. That is why the rubric belongs at the design stage rather than the drafting stage.
One boundary, stated plainly. We help you design, plan, analyse and write. The experiment is yours to run and the data must be yours. We do not fabricate results, we do not supply invented data sets, and where a course carries a proctored objective assessment we prepare only and never sit or assist during it, and never ask for portal credentials.
Six mistakes that cost a term in C683
- Choosing a question that produces opinions instead of numbers. If you cannot state the unit of your dependent variable in one word, redesign before you start.
- Running too few trials. Two measurements cannot show a pattern. More repetitions of a simple design beats one elaborate run every time.
- Leaving controlled variables uncontrolled. Two plants on different windowsills differ in light, temperature and draught, so whatever you thought you were testing, you were not.
- Writing methods as a story. Past-tense narration of your weekend is not a procedure. A reader must be able to follow it and get your setup.
- Interpreting inside the results section. It muddles two scored aspects at once and makes both harder to credit.
- Starting the write-up before the data is complete. A report shaped around the result you expected is very hard to reshape around the result you got.
How support works on this course
The highest-value moment in C683 is before anything is set up. Send your Course of Study materials and your draft idea, and you get a design review against your own scored aspects: whether the question is testable, whether the measurement produces usable numbers, how many trials the design needs, and what to control. Fixing a design on paper takes an hour. Fixing it after three weeks of data collection takes the three weeks again.
Once the data exists, help means analysis and drafting: choosing the figure that shows what you found, separating results from interpretation, writing a method someone else could repeat, and building a limits section that reads as scientific judgment rather than as apology.
Questions students ask about C683
Is C683 the same course as SCIE 1001?
Do I have to design my own experiment?
Can you run the experiment or supply data for me?
Still choosing an experiment?
Send your Course of Study materials and the idea you are considering. You get a design review against your scored aspects before you spend three weeks collecting the wrong data.
Where C683 sits in WGU's programs
The July 2026 catalog places this code in 11 current WGU programs. Open a program page for the complete standard path and term positions. The live Degree Plan remains authoritative after transfer credit, substitutions, and mentor planning.
The assessments, one by one
The public catalog does not publish this course's PA/OA identity or task count. WGU Tutors publishes at most one PA manual per course and only from a WGU-controlled public rubric. Until that source exists, PA help begins from the student's real Course of Study and OA support remains preparation only.