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Identify and fix errors in data collection

TX.S.2.A

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Classification of data

Characteristics of Quantitative Data

Classify the Level of Measurement

Identify the Level of Measurement

TX.S.2.B

Influencing factors in data collection

TX.S.2.C

Spread of a data set - standard deviation & variance

TX.S.2.D

Sampling distributions

Understanding Proportions and Sample Size

TX.S.3.B

Frequency distribution and histograms

Regression analysis

Equation of the best fit line

Stem and leaf plots

The Leaf and Stem of Numbers

Recognizing the Leaf and Stem of Numbers

Recognizing The Leaf And Stem of Numbers Leaf

Recognizing The Leaf And Stem of Numbers Stem

Reading Stem and Leaf Plots — Median

Reading Stem and Leaf Plots — Range

stem and leaf plots Range

Reading Stem and Leaf Plots

Reading Stem and Leaf Plots — Mean

Reading Stem and Leaf Plots — Mode

Frequency polygons

TX.S.3.C

Range and outliers

Range and Outlier Word Problems

Outlier Word Problems

Range Word Problems

Find the Range

Shapes of distributions

TX.S.3.E

Center of a data set: mean, median, mode

TX.S.3.F

Probability with Venn diagrams

TX.S.4.B

Comparing experimental and theoretical probability

comparing experimental and theoretical probability

TX.S.4.C

Probability distribution - histogram, mean, variance & standard deviation

TX.S.4.D

Central limit theorem

Central Limit Theorem

Applying the Central Limit Theorem

TX.S.5.A

Confidence levels and critical values

TX.S.5.B

Margin of error

Margin of Error

TX.S.5.C

Binomial distribution

Binomial Distribution

Mean and standard deviation of binomial distribution

Interpreting Mean and Standard Deviation of Binomial

Confidence intervals to estimate population mean

TX.S.5.E

Making a confidence interval

TX.S.5.F

Null hypothesis and alternative hypothesis

TX.S.5.H

P-value hypothesis testing

Proportion Hypothesis Testing

TX.S.5.I

Traditional hypothesis testing

Traditional Mean Hypothesis Testing

TX.S.5.J

Type 1 and type 2 errors

Type 1 and Type 2 Errors

TX.S.6.A

Sampling methods

Sampling Methods

TX.S.6.D

Student's t-distribution

Student"s t-distribution

Determining a Confidence Interval for a Population Mean using t-distributions

Census and bias

Census and Bias

Classifying Bias

TX.S.6.E

Bivariate, scatter plots and correlation 

TX.S.6.F

Proving claims

Confidence levels, significance levels and critical values

Categorical and quantitative data

Inference

Probability and random variables

Statistical studies

Variability

math

- Hi, welcome to this question right here. So we're trying to rewrite this question so that they don't have a bias. Okay? So that means there's a problem with the question itself already. We just have to figure it out. So the question here says, 
"What is your most favorite dessert?" We&apos;ve got A chocolate cake, B strawberry cake and C vanilla cake. Okay? 
Any problem with this question? The thing here is this, it's only asking you the most favorite dessert, okay? Now we don't see the following selections right here. When I ask you this question, "What's your most favorite dessert?" You're probably maybe thinking like, "Okay, I like jellos. I may like the chocolate cookies. I may like the yogurt." Then you see all the selections here, you'll be like, "Oh, what's going on?" I only have the chocolate cake, strawberry cake and vanilla cake to choose from. So that's kind of bias. You are leaded to just the cake. You're assuming that everybody likes the dessert cake. Okay? 
So that's not really fair. Okay? So what happen is, we want to eliminate that-- 
this part right here. The most favorite dessert, and then they're thinking-- the survey is thinking that or trying to lead you that it's the cake that you like the best. Okay? Because you maybe like something else but they're trying to lead you to the cake. Okay, so this is probably done by the cake company and turned it into a survey. So what happened is, what we can do is this. We're going to change the question to something like, "Which of the following cake you like the most?" This is more reasonable. It kind of puts your own preference away and actually puts yourself in another perspective that okay, we&apos;ve got a list of cakes here for you. Okay? You don't have your own preference. These are the cakes for you and you're just going to pick something you like the most. Okay? 
So what happened here is, that if you read it again it says, "Which of the following cake you'll like the most?" We&apos;ve got a chocolate cake, strawberry cake and vanilla cake. So right now you just-- 
When you jump into this part here, you'll know that you're picking the cakes. Just the cakes, and you're picking the one that you'd like the most. Whereas the first one, it kind of leads you to the problem where you're actually-- They're trying to lead you that, assuming that everybody likes the cake. Okay? So that's not a good part right here. So the most favorite dessert right here, this dessert part should change to, give you a list of cakes right here. Okay? This is a much better question comparing to the first one. Pretty tricky, but if you read it a couple of times you will see the difference. Okay? Thanks for watching.

The results of researches are heavily affected by the process of data collection. However, the process is prone to errors. For example, biased and misleading survey questions, and leading survey questions can all affect the results greatly. In this lesson, we will learn how to identify and fix these errors.

Influencing factors on data collection

Influencing factors in Data Collection

Application of averages