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The transcript explains measurement accuracy as closeness to the true value, introduces random, systematic, and gross errors, and shows how repeating measurements can improve accuracy only when systematic error is absent. It then distinguishes precision from accuracy by emphasizing internal agreement among repeated measures and explains reliability as measurement stability, linked to precision and random error.","Science – Investigating Science – Evaluating first hand investigations transcript\u000b \u000b(Duration 21 minutes 41 seconds)\n(gentle music)\nInstructor: Hello, and welcome to this presentation on planning and conducting investigations. This presentation was put together for Investigating Science, but it may be used for any Stage six science subject. Firstly, I would like to pay my respect and acknowledge the traditional custodians of the land on which this meeting takes place, and also pay my respect to elders both past and present.\nThis presentation accompanies the document ‘Investigating Science guides and scaffolds for secondary source and first-hand investigations’. As indicated by the title, we will be looking at secondary source research and how to evaluate secondary information. We'll be looking at how to evaluate quantitative data and we will also look at some scaffolds and self-evaluation checklist for firsthand investigations. The page numbers you see listed, there are those from the accompanying document.\nSo now we're moving onto the second topic; evaluating data. So first up, we're going to look at accuracy, which can also be thought of as exactness. So the true value of a measurement cannot be accurately known. So really all measurements are estimations. So error is a difference between the measured quantity and the true value. So the smaller, the error, the closer the measurement is to the true value and the more accurate your measurement is. Now there are different types of errors. So, the first step we have random error. It's just normal, natural part of everyday life because we can't actually measure the exact true value. Now, the great thing with random errors is that when we take multiple measurements and take an average, that average will be closer to the true value. So that's why repeating measurements and taking an average improves accuracy. Now over here, we have a picture of a domino in the top side and we've put a ruler up. And as you can see, it's a bit hard to tell, is it actually, where is it between 43 or 44 millimetres? So the true value might be 43.24, but we'll never be able to measure that. We just know it's within that range. And so that's an example of random error.\nAnother type of error is called systematic error. So this is where there's some problem in the setup or method, and so you're out by the same amount every time. So if we have a look at the bottom domino here, instead of measuring the domino from the zero millimetre point, we're actually measuring it from the end of the ruler. So what that means is every single measurement is going to be out by the same amount. And it doesn't matter how many times we repeat the measurement, if we're using that same method, all our measurements will be out and we're not going to be able to improve accuracy by taking an average. And the third type of error is called a gross error. You might know of them as blunders. Often they're the cause of outliers in your data.\nSo next, we're going to take a quick look at precision. Now people commonly mistake this for accuracy, but it is a different thing. Precision is actually internal reliability. So this is probably what you think of when you're talking about reliability of an experiment because measurement precision is the extent to which repeated measurements made under the same conditions, agree with each other. In terms of instrument precision, so this is related to the error associated with the instrument itself. Now it's important to know that digital instruments do not always have greater precision or lower error than analogue instruments. So for example, you might have an electronic scale at school that fluctuates a lot when you're trying to measure something. So that has very low precision.\nSo this is another way to look at accuracy and precision. So we can see on the diagram here, this blue dot represents the true value. And then we are trying to measure this value and we can see the red Xs represent all the","cbCaihR3RjSJ7ccb","https://ap.wps.com/l/cbCaihR3RjSJ7ccb","docx",79386,7,"English","en",105,"# Planning and conducting investigations\n## Evaluating secondary information and quantitative data\n# Evaluating data\n## Accuracy and types of error\n## Precision and instrument considerations\n## Reliability","[{\"question\":\"Why is repeating measurements and taking an average sometimes enough to improve accuracy?\",\"answer\":\"Taking an average reduces random error because the average of multiple measurements moves closer to the true value. This improves accuracy only when systematic error is absent.\"},{\"question\":\"What is the difference between accuracy and precision?\",\"answer\":\"Accuracy refers to closeness to the true value and depends on random plus systematic errors. Precision refers to internal agreement between repeated measurements and mainly depends on random error.\"},{\"question\":\"How do random, systematic, and gross errors affect measurements?\",\"answer\":\"Random errors vary naturally and can be reduced by averaging. Systematic errors shift measurements in the same way every time, so averaging cannot fix them. Gross errors (blunders) often create outliers in data.\"}]","Science - Investigating Science - Evaluating First Hand Investigations Transcript | DOCX",1788259392,3,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":14,"keywords":34,"description":15,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"science-investigating-science-evaluating-first-hand-investigations-transcript","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":11},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/template/","Template",2,{"item":49,"name":13,"@type":43,"position":29},"https://docshare.wps.com/template/presentations/",{"item":51,"name":14,"@type":43,"position":52},"https://docshare.wps.com/template/science-investigating-science-evaluating-first-hand-investigations-transcript/169639/",4,{"url":51,"name":14,"@type":54,"author":55,"headline":14,"publisher":57,"fileFormat":60,"inLanguage":23,"description":15,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/vnd.openxmlformats-officedocument.wordprocessingml.document","2026-09-05","2026-09-01",true,{"@type":65,"interactionType":66,"userInteractionCount":29},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is repeating measurements and taking an average sometimes enough to improve accuracy?","Question",{"text":75,"@type":76},"Taking an average reduces random error because the average of multiple measurements moves closer to the true value. This improves accuracy only when systematic error is absent.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the difference between accuracy and precision?",{"text":80,"@type":76},"Accuracy refers to closeness to the true value and depends on random plus systematic errors. Precision refers to internal agreement between repeated measurements and mainly depends on random error.",{"name":82,"@type":73,"acceptedAnswer":83},"How do random, systematic, and gross errors affect measurements?",{"text":84,"@type":76},"Random errors vary naturally and can be reduced by averaging. Systematic errors shift measurements in the same way every time, so averaging cannot fix them. 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